DE10234676A1 - Procedure for electromagnetic modification of brain activity to cause predetermined mode of behavior using behavior model - Google Patents

Abstract

The procedure uses model of brain activity, which describes influence of exogenic electrical and/or magnetic fields on brain activity and uses behavior model which describes the association between the brain activity and behavior. Head unit has measurement system with grid of EEG or MED sensors, data preprocessing and data transmission to intermediate computer. Transmitter base unit is computer with database containing model and user data. To determine nominal course of brain activity from target behavior model used is a variant of Davidson-model, Ishihara-Yoshie-model or chaos-onset model.

Description

The invention relates to methods and a device for the model-based controlled or regulated electromagnetic Modification of brain activity in vivo and the resulting behavior modification.

definitions:

Model-based controlled or regulated Modification means deliberate change or no change of brain activity on the basis of a behavior goal, a behavior model, and of a brain activity model.

Regulated means that observation, Calculation and modification steps in feedback loops with each other are connected.

The behavior goal to be named by the user includes type and expression of a certain behavior and its permanence.

Behavior refers to everyone spatiotemporal combination of any potentially validable factual or possible conditions one person. Behavior includes, but is not limited to, perception, Acts, motivation, attention, memory, learning, awareness, Emotions, thinking, cognition, mental states, skills, personality traits.

Behavior is based on brain activity. The relationship between the two is quantified within the framework of a behavior model, ie. an empirically established link between certain behaviors with a certain dynamic of certain brain activity characteristics.

Brain activity characteristics are off Parameters of measurable values of Brain activity. examples for Brain activity characteristics are potential differences between an EEG electrode and a Reference electrode, or the band area of the most pronounced across all electrodes Frequencies ("alpha band" or similar), or the similarity index ( "Similarity index ", see [5]) of the measured values of an MEG sensor.

A brain activity model refers to physiologically based model that mediates brain activity the dynamics of brain components and / or their interactions describes (which includes e.g. Hodgkin-Huxley [24, FitzHugh-Nagumo], Wilson-Cowan [1], or Jirsa hook [18]).

Conventional models are not specific, but generic, because not observable about population means estimated become.

A non-observable is a parameter or a variable of a generic model that is in vivo only with considerable effort - or not at all - with the necessary spatial and temporal resolution could be measured. Unobservable determine the brain dynamics, and fluctuate between individuals and within an individual up to one Factor of one hundred. Generic brain activity models make no statements about direct ones External influence on the dynamics of brain components, at best via indirect ones External influence through receptor stimulation, deprivation, or lesions.

The brain activity models used below, which are generally specific, quantify, among other things, external influences via electrical and / or magnetic fields, and are based on non-observables that are relevant for a user in a time interval and determined using suitable methods. These brain activity models usually include nonlinear differential equations, for example of shape, for each brain component ρ exo = 2 * sin (2πt * 10/1000) ,

Here x is a brain activity characteristic, ? whose time derivation, t time, f function, among other things, of Art of the component ("local Parameters "), the type and scope of the physiological connections spreading influence of other components ("endogenous input"), and the direct external influence on the brain component ("exogenous Input "). Depending on the model used, the endogenous input includes further ("translocal") parameters, such as coupling strengths or delay times between components.

Missing or insufficient specification of the brain activity model leads to the respective user to a wide spread in this case largely unknown Effects of exogenous electromagnetic inputs and therefore inapplicability corresponding procedures outside an area of neurological, psychiatric, or psychological damage, where random effects are weighed against possible treatment success can be.

Electromagnetic means electrical and / or magnetic quantities and relates both to the type of incoming observation data (essentially electromagnetic correlates of the brain activity of the user), and to the essential mode of modification (for example, by means of an extracranially generated changing magnetic field, which effortlessly generates the cranium of the Penetrates the user and causes intracranial induction voltages, which has an influence on the brain activity of the user given is).

In vivo refers to use the procedure for living users, in contrast to cell preparations, Brain parts, computer simulations or the like, and thus represents special requirements both with regard to the complexity of the taking place Brain processes, as well as the speed of observation, calculation and modification as well as security of the process.

With the current state of the art, model-based controlled or regulated electromagnetic modification of brain activity in vivo is not possible (the human brain consists of approximately 10 ¹¹ neurons and an unknown number of connections between them), and has not been considered or attempted to date , The same applies to the associated behavior modification.

• – einen nichtlineare Dynamik/neurale Netze/künstliche Intelligenz-Ansatz (im folgenden als "NA" bezeichnet), bei dem bestimmte Eigenschaften des lebenden menschlichen Gehirns in vereinfachter Weise nachgebildet werden sollen, um Maschinen mit ähnlichen Eigenschaften zu bauen (sog. „Neurocomputer"),
• – einen kontrolltheoretischen Ansatz (im folgenden als "CON" bezeichnet), in dem Prinzipien der mathematischen Kontrolltheorie auf engumschriebene Teile einer Gehirn-Körper-Einheit angewendet werden,
• – einen medizinischen Ansatz (im folgenden als "MED" bezeichnet), der sich mit praktisch-klinischen Verwendungszwecken von elektromagnetischen Feldern (im wesentlichen) hoher Feldstärken bei Menschen oder Tieren befasst.

There are three scientific / technical development directions with few overlaps related to brain activity or its change:

• - a nonlinear dynamics / neural networks / artificial intelligence approach (hereinafter referred to as "NA"), in which certain properties of the living human brain are to be simulated in a simplified manner in order to build machines with similar properties (so-called "neurocomputers" )
• A control theory approach (hereinafter referred to as "CON") in which the principles of mathematical control theory are applied to narrowly defined parts of a brain-body unit,
• - A medical approach (hereinafter referred to as "MED"), which deals with practical clinical uses of electromagnetic fields (essentially) high field strengths in humans or animals.

A possible basic building block of NA is the neural oscillator, i.e. an entity that can alternate between oscillatory behavior and rest, and that can be composed of an ensemble of smaller entities (which will not be considered further). NA deals with neural networks, i.e. small (order of magnitude <10 ⁴ ) connected ensembles of dynamic oscillators, and the phenomena that occur in these networks (e.g. memory or pattern recognition). After functional principles were originally extracted from living organ nerve systems, NA is increasingly moving in the direction of artificial systems based on the extracted principles. An example of this development is [2].

CON moves in the opposite direction, after this approach with the application of linear control to sufficient simple mechanical systems started. Linear control to nonlinear or stochastic systems (such as a human brain) is not only unsuitable, but also potentially dangerous, because with linear control the control force is proportional to the desired one change is (and thus become very large can).

Control procedures can be in Classify model-based and data-based procedures. The quality of one model-based procedure is linked to the suitability of the model for the problem in question. To date, in the human area or no brain activity in animals or behavior models.

• – hohe bis unerfüllbar hohe Anforderungen an Speicherplatz und Rechnergeschwindigkeit,
• – potentiell beliebig lange Wartezeiten bis zum Erreichen eines Zielorbits,
• – intermittierende Ausbrüche aus dem Zielorbit in Systemen mit stochastischen Elementen,
• – Einschränkungen des Kontrollbereichs in Systemen, in denen nur aggregiertes Verhalten meßbar ist,
• – Einschränkungen des Kontrollbereichs in Systemen, in denen die Kontrollkraft verschmiert wird,
• – Betrag der Kontrollkraft im Inneren stark heterogener Systeme unbekannt.

The quality of a data-based process depends on the simplicity of the system to be checked (since the first step in a data-based process is usually the reconstruction of the phase space, in which search procedures are then carried out and iterations are calculated - which is the concrete calculation for higher-dimensional phase spaces Makes real time impossible, with the exception of time-delayed feedback, in which there are waiting times until a target state to be stabilized is reached). Current control methods cannot be used for controlled or regulated electromagnetic modification of brain activity in vivo, since several of the following effects add up:

• - high to unachievable high demands on storage space and computer speed,
• - potentially arbitrarily long waiting times until a target orbit is reached,
• Intermittent bursts from the target orbit in systems with stochastic elements,
• - restrictions of the control area in systems in which only aggregated behavior can be measured,
• - restrictions of the control area in systems in which the control force is smeared,
• - Amount of control power inside highly heterogeneous systems unknown.

MED is implicitly based on a microscopic physiological model with individual neurons as constituents of brain activity - which will be called "individual neurons" in the following. Accordingly, in vivo non-invasively unobservable variables such as ion concentrations on both sides of the cell membrane, number of axons etc. play an essential role in calculating the number of nerve impulses per unit of time (fire rate). For an automatic, individualized, controlled or regulated electromagnetic modification of brain activity in vivo, the single-neurological paradigm (with its 10 ¹¹ single neurons and an unknown number of connections between them) is unsuitable. As a result, the MED approach based on it is affected by the apparent inexplicability or randomness of its clinical results pregnant (for transcranial magnetic stimulation - TMS - for example, see overview [3]). Other MED procedures that change brain activity are intra- and extracranial electroshocks. All MED methods aim to depolarize neuron membranes and thus to generate more action potential (so-called "stimulation").

In CON terminology you can the electromagnetic depolarization of individual neurons using MED procedures referred to as "enslavement" (where sensory enslavement by rhythmic flashes of light, acoustic signals, etc. should not be considered). Enslavement is the result of applying high intensity artificial signals to a System that then during this application from the outside takes predetermined signal patterns instead of its natural behavior. In NA it is known that under weak coupling, communication in neural networks depends on the commensurability of the frequencies with which the participating neurons oscillate. Hence local Enslave with a frequency that is not commensurable or only slightly commensurable the elimination of the enslaved neurons from their normal communication, and so creates a virtual lesion. Current MED Procedures consider virtual lesions as an essential effect of TMS - the single-ural paradigm inexplicable is.

MED procedures consider "stimulation" than improved when less high intensity is needed to single neurons to move to fire, or the one caught by the enslavement Area stronger can be narrowed down. Repeated enslavement on the same medical meaningful locus can physiological outlast the application period changes condition, which in some cases is positive are (e.g. statistically significant improvement of certain types clinical depression with repeated TMS, see e.g. [3]).

In use in humans current The process is individualized only in two ways: firstly with Finding a suitable locus for the individual neurons to be depotized, secondly, finding a reference value for the strength of the one leading to enslavement electromagnetic field (specified as a percentage of a "motor threshold"). In a newer MED procedure [4], TMS is used diagnostically, about the individual existence or nonexistence of physiological neural connections between different brain areas, as well as, with restrictions, transmission speeds between different brain areas.

Current MED procedures serve preferably medical uses, in which the disadvantages enslavement (up to the unlikely case of seizures) whose medical effects are at least compensated. Current MED Procedures only include passive security measures such as avoiding high-frequency enslavement, excluding patients with a tendency to epileptic seizures, Switch off the electromagnetic field in the event of a seizure should occur.

In summary: With current techniques and method can be model-based, controlled or regulated electromagnetic modification of brain activity cannot be achieved in vivo, these techniques and procedures are still moving towards this accessibility hin: MED is stuck in the single-ural paradigm, NA is moving on building artificial Systems, and CON suffers from in vivo inapplicability to complex heterogeneous systems.

Object of the present invention is to process and an apparatus for model-based, controlled or regulated electromagnetic modification of brain activity in vivo and create the resulting behavior modification.

The task is accomplished through procedures with the features in claim 1 and by a device with the Features solved in claim 44. Advantageous embodiments of the invention are specified in the subclaims.

The use of a brain activity model, and determination of the individual, possibly intra-individual and / or time-dependent Non-observables, as well as determination of the individual, possibly intra-individual and / or time-dependent translation operators between extracranial signal and control force allow one secured intervention within the framework of a control or regulating circuit, which in turn is a reliable one Achieve a brain activity goal causes. Using a behavioral model ensures that achieving a brain activity goal is achieving individual behavioral goal of the user. The invention is based on the knowledge that with behavior models the connection between behavior and the dynamics of brain activity characteristics is quantified, and with suitably individualized brain activity models again the dynamics of brain activity characteristics, specifying suitable ones Control parameters are quantified so that a reliable intervention is possible to achieve an individual behavioral goal.

The invention is exemplified below explained using the drawings. The drawings show:

1 a transmitter in a side view,

2 the transmitter off 1 in a bottom view,

3 a planar projection of sensors and transmitters according to their arrangement on a helmet,

4 a helmet and support axle along with a chin rest, and

5 another planar projection of sensors and transmitters according to their arrangement on a helmet,

6 principle interaction of head unit, intermediate unit, base unit,

7 HAC model modes with variation of _Δ (extracts),

8th HAC model modes with variation of a (excerpts),

9 HAC model modes with variation of b (excerpts),

10 HAC model modes with variation of c (excerpts),

11 HAC model modes with variation of d (extracts),

12 Similar HAC model modes to two different model modes I and II (example),

13 HAC model modes for I under incommensurable exogenous inputs of various strengths without elementary loops (examples),

14 HAC model modes for II under incommensurable exogenous inputs of different strengths without elementary loops (examples),

15 HAC model modes for I under commensurable exogenous inputs of various strengths without elementary loops (examples),

16 HAC model fashions for II under commensurable exogenous inputs of different strengths without elementary loops (examples),

17 HAC model modes for I with exogenous input of various strengths with elementary loops (examples),

18 HAC model modes for II with exogenous input of different strengths with elementary loops (examples),

19 a theta-HAC model mode with various endogenous inputs and an exogenous input with elementary loop (examples),

20 Flow diagram of the entire process,

21 Flow chart of the S 2000 procedure (calibration),

22 Flow chart of the method S 2100 (local calibration, part 1),

23 Flow chart of the procedure S 2100 (local calibration, 2nd part),

24 Flow chart of method S 2300 (translocal calibration),

25 Flow chart of the S 3000 method (controlling and / or regulating brain activity).

Contraption:

• – eine oder mehrere Kopfeinheiten, verbunden mit
• – einer Zwischeneinheit, von denen eine oder mehrere verbunden sind mit
• – einer Basiseinheit (6).

The device according to the invention comprises

• - one or more head units connected to
• - An intermediate unit, one or more of which are connected to
• - a base unit ( 6 ).

A head unit comprises a measuring system with devices for electromagnetic measurement data acquisition ("sensors"), Measurement data preprocessing and measurement data transfer to the intermediate unit, and an actuating system with devices for generating electromagnetic Fields ("transmitters"), and a device for implementing the intermediate unit outgoing digital control or regulation specifications in transmitter signals. An intermediate unit comprises a computer with software with which the below closer explained Procedure for controlling brain activity when calibration is not necessary be implemented, as well as connections to and from the base unit. A basic unit comprises a computer, which is a database containing the model and user data, and secondly the rest below shown in more detail Procedures are implemented. The intermediate and base units are open the same or localized on different computers.

An embodiment of the device has a head unit, an intermediate unit, and a base unit. The head unit includes, for example, a measuring system with an EEG cap with its extracranial sensors, connections to the amplifier, amplifier, connections to the A / D converter, A / D converter, connections to the intermediate unit, and an actuating system with extracranially attached current-carrying coils as transmitters . controllable power supply for these transmitters, together with connections, D / A converter, connection from the intermediate unit. The intermediate unit comprises, for example, a PC with a screen and input keyboard, as well as software and connections to and from the base unit. The base unit includes, for example, a powerful computing unit.

Suitable sensors are, for example EEG or MEG sensors. The MEG sensors are e.g. from a SQUID sensor element with a suitable evaluation device for detecting a magnetic field and cooling device educated. The EEG sensors e.g. two electrodes for measuring an electrical potential difference.

An advantageous embodiment of a Sensor includes its partial or complete electrical and / or magnetic shielding from its surroundings, as far as it does not hinder its function becomes.

An advantageous embodiment of the parts of the measuring system close to the head includes a variety of sensors that are intracranial or extracranial are distributed, this multitude of sensors is called a sensor grid designated.

An advantageous embodiment of a extracranial sensor grid includes its fixation with respect to the Crane of the respective user, so that with multiple opening and When the sensor grid is set down, the sensors change their relative position Take up the position again, for example by fitting the sensor grille in a helmet, the inside of which is the cranial shape of the user replicates.

Another advantageous embodiment of the sensor grid implanted electrodes.

An advantageous embodiment of an extracranial transmitter comprises a current-carrying coil with a para-, dia- or ferromagnetic core, as in FIG 1 shown in side view, the arrow directions symbolize the directions of the current flow, and an embodiment is shown in which the transmitter is attached to a holder, not shown, via mechanical elements. 2 shows this configuration seen from a cranial direction.

An advantageous embodiment of a extracranial transmitter includes its protection against deformation, for example by pouring the live Parts in suitable resin, or embed the current carrying Parts in sturdy insulating material.

An advantageous embodiment of a extracranial transmitter includes cooling devices.

Another advantageous embodiment an intracranial transmitter includes implanted electrodes.

An advantageous embodiment of a Transmitter includes some or all of its electrical and / or magnetic shielding from its surroundings, so far thereby its function is not hindered.

An advantageous embodiment of the parts of the positioning system close to the head comprise a large number of transmitters, that are intracranially or extracranially distributed, this multitude of transmitters is referred to as a transmitter grid.

An advantageous embodiment of a extracranial transmitter grid includes its fixation with respect to the Crane of the respective user, so that with multiple opening and If the transmitter grid is set down, the transmitters set their relative values Take up position again, for example by fitting the transmitter grille into a helmet, the inside of which is the cranial shape of each Simulates the user.

Another advantageous embodiment of the transmitter grating implanted electrodes.

An advantageous embodiment of the parts of an extracranial measuring and positioning system close to the head comprises a helmet on its inside which imitates the cranial shape of the respective user with connecting cables running through a carrier axis, sensor and transmitter gratings being fixed inside the helmet in such a way that the two gratings overlap - ie there are enough transmitters in the vicinity of each sensor and vice versa. A planar projection of a mechanical holder of this embodiment shows 3 (openings for sensors are shown as circles, openings for transmitters are angular). In the embodiment described, the user sits on a height-adjustable armchair with armrests and neck support below the helmet. Helmet and carrier axis along with a chin rest as an additional fixation aid are in 4 shown.

In another embodiment the sensor grid intracranial and the helmet contains the extracranial transmitter grid.

In another embodiment the transmitter lattice intracranial and the helmet contains the extracranial Sensor grid.

Are in another embodiment both sensor and transmitter grids intracranial.

In an advantageous embodiment let yourself locally change the sensor density of an extracranial sensor grid. In In a further advantageous embodiment, this change takes place automated, controlled or regulated via the intermediate unit.

In an advantageous embodiment, the transmitter density of an extracranial transmitter grid can be changed locally and / or the angle of inclination of each individual transmitter to the patient's cranium change. A planar projection of a mechanical holder of this embodiment shows 5 (openings for sensors are shown as circles, openings for transmitters are angular). Here it is possible to anchor several transmitters in the cross-shaped openings of the holder and / or to tilt the transmitter relative to the holder. (Among other things, all conventional coil configurations can be emulated in this embodiment). In a further advantageous embodiment, this change is automated, controlled or regulated via the intermediate unit.

Setup, functional tests and maintenance The extracranial device is done by technicians who have no must have medical training.

In an advantageous embodiment is the device with conventional protection against power failures and / or Provide voltage fluctuations.

In an advantageous embodiment run additionally on the intermediate unit real-time and automatic procedures for Density and positioning optimization of sensors and transmitters.

In an advantageous embodiment run additionally on the intermediate unit real-time and automatic procedures for Elimination of artifacts, i.e. non-random distortion of measured values, by artificially generated magnetic fields, eye movements, muscle twitches etc. arise.

The relationship between head unit, intermediate unit, and base unit in the simplest embodiment is shown in 6 symbolizes.

Method:

The process according to the invention comprises in detail the process steps specified below, explained in more detail below and marked with "S" (input and output data of the steps are marked with "D"). The relationship between the process steps S 500, S 800, S 2000 and S 3000 is shown in 20 shown in a flowchart, rectangles representing process steps, diamond decision steps, and parallelograms input or output data of process steps.

S 500 In this step, the quantities necessary for the reliable achievement of the stated behavior goal (D 2000) and their dynamics are specified with the help of a behavior model (D 3000).
The behavior model represents the connection between behavior and the course of certain HAC. ("HAC" in the following stands for both singular and plural for "brain activity characteristic").
Examples of HAC: potential differences measured by a sensor (electrode plus reference electrode), or power spectrum of the signal received by the sensor within a time window, or quotient of beta (13–30 Hz) - EEG activity to alpha (8–12 Hz) - EEG Activity for this sensor (see also [11]).
Example of a behavior model: Reduced Davidson model (see eg [11]), according to which positive emotions with a higher quotient from beta (13–30 Hz) EEG activity to alpha (8–12 Hz) EEG activity in the left front Cortex go along.
The behavior model (D 3000) allows the behavior goal (D 2000), which basically includes the durability of the desired behavior, to be converted into a target course of the HAC (D 2100), and thus quantified.
The type and number of HAC results in minimum equipment requirements (D 4000). Example: To calculate the quotient from beta (13–30 Hz) - EEG activity to alpha (8–12 Hz) - EEG activity in the left-frontal cortex you need at least one EEG electrode (plus a reference electrode). At least one transmitter is required to achieve the desired course of the above-mentioned HAC in a controlled manner.

S 800 The HAC used must be calculable from the variables of the generic brain activity model used (D 1000).
Example: HACs based on Fourier decomposition for a limited brain area can be calculated from the dynamics of the neural oscillators of the Wilson-Cowan model (see eg [1]).
The HAC results in minimum calibration requirements (D 1100) with regard to the generic model used. The less robust the calculation of the HAC is, the greater the demands on the calibration. If the HAC are based on coupled, non-robust variables from different local origins, the local calibration must be supplemented by translocal ones. In any case, the type (eg "none, local, translocal", D 1110) and scope (eg list of sensors and transmitters, D 1120) must be named.
Examples: Calibration is not absolutely necessary for the robust, non-linear similarity index (see, for example, [5]). Local calibration of the Wilson-Cowan model is required for the above beta / alpha quotient. In the case of complex cognitive performance (e.g. artistic creativity, see [19]), translocal calibration is usually required.

S2000 calibration comprises the determination of individual, possibly time-specific values for the parameters, endogenous input, estimated in the generic brain activity model from population mean values, and the quantification of the likewise individual, possibly time-specific, exogenous influence of electromagnetic fields on brain activity. ("Electromagnetic" stands for "electrical and / or magnetic" and is abbreviated to "EM" below.)
Calibration results in a specific brain model that quantitatively captures exogenous input as a control variable in the model's equations. Calibration is detailed in 21 ,
In the simplest case, a distinction is made between local (see 22 - 23 ) and translocal (see 24 ) Calibrate. The first goal of the local calibration is to limit for each sensor for a certain user in a certain time interval which brain activities take place in the detection range of the sensor (process shown in 22 ). For this purpose, EM brain activity is measured with this sensor (S 2110). The resulting time series is converted into an actual course by HAC (S 2120).
A model mode is a solution of the differential equations of the brain activity model for a set of non-observables, ie parameters, endogenous and exogenous inputs. Theoretical HAC curves (HAC model modes) can be calculated from each model mode. Since HAC usually have a simplifying effect, it is possible that several model modes result in similar (ie indistinguishable within error bounds) HAC model modes. It is also possible that several sets of non-observables result in the same model mode. This connection between HAC model modes, model modes, and sets of non-observables also occurs in calibration theories, such as electrodynamics (corresponding to a HAC here, for example, "energy density in a space sector", corresponding to the model modes different electrical and magnetic fields that this energy density generate, and according to the sets of non-observables different potentials, which in turn can be the basis of the electric magnetic fields).
Model modes and possibly HAC modes are stored in a database and / or can be calculated quickly. There are only a finite number of sets of non-observables stored (grids), the same for model modes and HAC model modes. If necessary, there is the possibility of refining and / or expanding the grid (for example by means of new parameter sets and / or new types and / or new coefficients of endogenous and / or exogenous inputs).
It is possible that a mode on the way from its wearer to the respective sensor is subject to weakening and / or distortion and / or phase shifts. (The "carrier" of a mode is defined as the neuron ensemble whose activity the relevant mode generates.) The image of the mode arriving at the respective sensor is referred to as the image mode, analogously for associated HAC modes.
In the above-mentioned actual course of HAC, the a priori assumptions (D 1040, 22 ) possibly changed images of HAC model modes identified (S 2130). In the simplest case, the image of a HAC model mode is proportional to the HAC model mode itself and can (except in cases of negligible amplitude) be identified with the quotient of the HAC model mode and the reciprocal of an attenuation factor. This simplest case is used in the following. In the simplest case, a (HAC) model mode is local and thus describes localized brain activity.
Decomposition according to S 2130 (see 22 ) includes finding HAC model modes in the empirical HAC history, identifying model modes possibly underlying these HAC model modes and identifying sets of non-observables that may be underlying these model modes, whereby only sets of non-observables with exogenous input equal to zero are permitted. Endogenous inputs are assumed to be constant for the purposes of local calibration, in the second simplest case as sinusoidal, in the third simplest case as oscillating.
The limitation to the examination of relevant HAC model modes is helpful for the practicability of the method. A HAC model mode found in the actual curves is said to be "relevant" if it can be determined in the actual HAC curve above predefined threshold values for noise, measurement errors, or the like.
The second aim of the local calibration is to find out for each brain activity in the measuring range of each sensor how this can be influenced by exogenous EM fields generated by at least one transmitter (shown in 23 ):
From the in 22 The method steps shown are transferred for each sensor and for each HAC model mode detected in the measuring range of this sensor, the list of parameter sets and endogenous inputs with which the generation of this mode is possible.
From these parameter sets with endogenous input (D 2400), those sets of non-observables which explain the respective HAC model mode with the test signal are now found out by testing (sending test signals with the aid of transmitters). This includes using the test signal to determine how the transmitter signal translates into exogenous input, that is, into a component of the model equations. As soon as this has been clarified, the influence of the exogenous signal on the corresponding model mode or HAC model mode can be calculated.
In the simplest case, a test signal results from the possible endogenous and exogenous inputs (comprising: constant, oscillating, with elementary loops, ie the time-delayed use of a measurement signal as a transmitter signal, and many others).
The test signal is integrated into the generic brain model as follows: replace "input" in the model equations with "endogenous plus exogenous input".
Exogenous input: = translation operator (transmitter signal)
The translation operator Ü represents the relationship between the exogenous, physically measurable signal and control variables in the equations of the specific brain model. Depending on the parameter set and endogenous input that are subject to the individual mode, as well as the shape and amplitude of the transmitter signal, different values of the control variable change the relevant mode , which in turn is physically measurable.
A test signal has the task of separating between different sets of parameters, endogenous inputs and, if necessary, translation operators for the HAC mode under consideration. A test signal (like every transmitter signal) is characterized by an external reference variable, as well as a generally time-dependent form at the location of the transmitter, in the variables described by the equations of the brain activity model, and a - possibly one-element - sequence of amplitude multipliers.
Each transmitter signal (and therefore also every test signal) is usually given here in hybrid notation, ie expressed in the variables of the brain activity model, with a sequence of amplitude multipliers, and by specifying an external parameter that specifies the signal generation.
Example: Extracranial generation of a magnetic field with 0.01 Tesla with the help of a coil through which a current of strength I ₀ flows. If the shape of the shape normalized to amplitude 1 with respect to a selected voltage unit is the voltage induced by the temporal changes in the magnetic field sin (2 * Pi * t * 5/1000) (voltage is assumed to be a variable of the brain activity model, t is time in milliseconds) , the same signal notation results for currents of magnitude I ₀ , 2 * I ₀ , 4 * I ₀ , 8 * I ₀ under otherwise identical conditions: (0.01T, sin (2 * Pi * t * 5/1000), ( 1,2,4,8)).

• – welche Modellmoden im Messbereich eines Sensors liegen,
• – welcher Parametersatz und welcher endogene Input die jeweilige Modellmode erzeugen,
• – wie die jeweilige Modellmode zu beeinflussen ist.

Zusätzlich ist es im Anschluß an lokales Kalibrieren im Schritt S 2300 möglich, identische Träger von Modellmoden zu identifizieren.
Für A_kl als eine dem Sensor S_k zugeordnete Mode, gekennzeichnet durch Parameter P_kl endogenen Input I_kl und Übersetzungsoperatoren Ü_kl,x , und A_mn als eine dem Sensor S_m zugeordnete Mode, gekennzeichnet durch Parameter P_mn, endogenen Input I_mn und Übersetzungsoperatoren Ü_{mn , χ} (χ Numerierung der Transmitter) gilt: wenn alle P_kl gleich den entsprechenden P_mn sind, und I_kl gleich I_mn, dann sind beide Moden identisch. Diese identischen Moden haben nicht denselben Träger, wenn es einen Transmitter Tχ gibt, so dass Ü_kl,χ ungleich Ü_kn,χ und/oder Ü_ml,χ ungleich Ü_mn,χ ist. Verfahrensschritt S 2300 ist nicht essentiell, bewirkt aber eine mögliche Verkleinerung der Funktionalmatrix (D 2410, s. 24) beim im folgenden dargestellten translokalen Kalibrieren.
Ziel translokaler Kalibrierung (S 2400) ist es, Art und Umfang des Einflusses anderer Moden und/oder sensorischen Inputs auf eine Mode festzustellen (Indirekte Fremdeinwirkung, ie. Darbietung visueller, akustischer, taktiler, und anderer Reize erfolgt über das sensorische System, bedingt Verzögerungen, und wird als Teil des endogenen Inputs modelliert). Werkzeug hierfür ist die „Funktionalmatrix": wenn insgesamt n verschiedene Modellmoden im Messbereich mindestens eines der Sensoren aufgefunden wurden, so ist diese Funktionalmatrix eine n*n Matrix , in deren (i,j)-ter Zelle ein Eintrag zu finden ist, wenn die Mode i auf die Mode j einen nachweisbaren Einfluss hat. Der Eintrag quantifiziert diesen Einfluss, sodass in den Gleichungen des Hirnmodells für die Mode j „endogener Input" ersetzt wird durch „gegebenenfalls zeitverzögerte Funktion der i-ten Mode plus sonstiger endogener Input".
Das Verfahren zum Füllen benötigter Teile der Funktionalmatrix ist in 24 dargestellt. Kenntnis der Funktionalmatrix oder von Teilen der Funktionalmatrix gestattet die Berechnung von mehre Konstituenten des betreffenden Hirnaktivitätsmodell umfassenden Phänomenen (bspw. Synchronisierung, Phase-Locking, und vieles mehr), wobei konventionelle numerische Verfahren, neurale Netze, nichtlineare Dynamik, und ähnliches verwendet werden.Another example: With z (t) as the system behavior normalized to voltage amplitude 1, for example, a signal is fed back by 10 milliseconds with currents analogous to previously as: (0.01 T, z (t-10), (1,2,4,8) ).
The duration of the partial signals (for I ₀ , for two times I ₀ , etc.) depends on the desired effect (e.g. transient versus limit cycle), as well as the possible pauses between two partial signals.
The transition from the transmitter signal to the signal on the carrier of the respective mode is subject to invariant influences and assumptions (D 1050).
The possible amplitude sequences are subject to technical and / or health conditions (D 4100, D 4200).
In the simplest case, for example, there are two different sets of parameters and endogenous inputs that lead to different model modes, but to similar HAC model modes. That is, by simply considering HAC, it cannot be decided which set of non-observables is subject to the brain activity in question. So for a shape of the test signal, a sequence of amplitude multipliers is calculated numerically using the generic brain model by inserting the two sentences (S 2160) in such a way that assumptions for the translation operator (e.g. combination of phase shift and attenuation of the signal) are made using a priori of the test signal, model modes are generated whose resulting HAC model modes are different for both sets.
This numerical calculation usually results in a number of possible shapes for test signals, each with a sequence of possible amplitude multipliers. From this set, an optimal test signal is then selected with the aid of predefined criteria (for example, minimal disturbance of all brain activities that occur, apart from the mode to be examined by the test signal).
One begins to send the signal (S 2170) with the first amplitude of the sequence, measures at least with the sensor S considered; and at most with all sensors of the sensor grid (see e.g. 5 ) the electromagnetic brain activity (S 2110), receives time series (D 2520), from which one can calculate the course of HAC.
The actual HAC curve is then decomposed according to HAC model modes with exogenous input (S 2135). Only parameter sets and endogenous inputs from D 2400 (see 22 ), whereby the "exogenous input equal to zero" is now replaced by a possibly other exogenous input, the shape of which results from the invariants and assumptions of influence, and the strength of which generally results from the (usually nonlinear) changes at different amplitudes of an amplitude sequence results.
If this decomposition yields several best-approximating sets of non-observables, then another test signal has to be calculated and the loop following S 2160 has to be run through again, with S 2135 no longer taking into account the sets of parameters and endogenous inputs from D 2400, but D 2410 (see 23 ). If multiple passes through the loop yield no results, the orientation of the transmitter must be changed or another transmitter selected.
Overall, after the local calibration is complete, it is known

• - which model modes are in the measuring range of a sensor,
• - which parameter set and which endogenous input generate the respective model mode,
• - how to influence the respective model fashion.

In addition, following local calibration in step S 2300, it is possible to identify identical carriers of model modes.
For A _kl as a mode assigned to the sensor S _k , characterized by parameters P _kl endogenous input I _kl and translation operators Ü _{kl, x} , and A _mn as a mode assigned to the sensor S _m , characterized by parameters P _mn, endogenous input I _mn and translation operators Ü _{mn , χ} (χ numbering of the transmitters) applies: if all P _{kl are} equal to the corresponding P _mn , and I _kl equal to I _mn , then both modes are identical. These identical modes do not have the same carrier if there is a transmitter Tχ, so that Ü _{kl, χ} not equal to Ü _{kn, χ} and / or Ü _{ml, χ} not equal to Ü _{mn, χ} . Method step S 2300 is not essential, but causes a possible downsizing of the functional matrix (D 2410, see 24 ) in the translocal calibration shown below.
The goal of translocal calibration (S 2400) is to determine the type and extent of the influence of other modes and / or sensory inputs on a mode (indirect external influence, i.e. presentation of visual, acoustic, tactile, and other stimuli takes place via the sensory system, due to delays , and is modeled as part of the endogenous input). The tool for this is the "functional matrix": if a total of n different model modes have been found in the measuring range of at least one of the sensors, this functional matrix is an n * n matrix, in the (i, j) th cell of which an entry can be found if the Mode i has a demonstrable influence on mode j. The entry quantifies this influence, so that in the equations of the brain model for mode j "endogenous input" is replaced by "possibly delayed function of the i-th mode plus other endogenous input".
The procedure for filling required parts of the functional matrix is in 24 shown. Knowledge of the functional matrix or parts of the functional matrix allows the calculation of phenomena comprising multiple constituents of the relevant brain activity model (e.g. synchronization, phase locking, and much more), using conventional numerical methods, neural networks, nonlinear dynamics, and the like.

S3000 The preferred EM control or regulation of brain activity via feedback based on brain activity model aims at achieving and maintaining target courses of HAC.
First, transmitter signals are calculated that are theoretically suitable for converting the actual curve of the HAC into the target curve. These transmitter signals are preferably composed of simple signals with an effect that can be calculated in the context of the specific brain model and possibly already tested in the course of the calibration, with both multiple effects (effects of signals from one transmitter on several modes) and spatial composition (signals from several transmitters). , as well as temporal composition (sequence of signals) come into question. After the feasibility check of the calculated signals (with regard to health limit values, equipment restrictions, possibly restrictions on propagation), the best transmitter signal is selected and sent using a utility function (e.g. minimum field strength per transmitter). The theoretical effect of the signal is known in a specific brain activity model with influence calibration and allows HAC prognoses. If there are significant deviations within the scope of the ongoing EM measurement and actual HAC calculation, check to what extent these deviations are due to changed endogenous input (and in this case modify the transmitter signal after corresponding recalculation). If this is not the case, S 3000 will be temporarily interrupted to improve the calibration. The corresponding procedure is in 25 shown.

The essential process steps are now explained in more detail using a few examples. The examples are run through the flowcharts ( 20 - 25 ) presented.

Example 1:

20 : D 2000 (behavioral goal):

Reinforce positive emotions for the long run the application.

D 3000 (behavior model):

As a reduced Davidson model (see e.g. [11]), the following relationship is referred to: positive emotions go with a higher one Quotients of beta (13-30 Hz) - EEG activity to alpha (8-12 Hz) - EEG activity in the left front Cortex.

S 500 (Specify HAC and their calculation. Specify target courses from HAC):

The Davidson model is based on the Performance spectrum of EEG signals.

(Annahme: nur ganzzahlige Frequenzen, Fourierfenster beispielsweise 500 Millisekunden, f_i Absolutbetrag des Fourierkoeffizienten der i-Hertz-Mode. Um die Frequenzen von 1 bis 50 Hz zu überdecken, sind nach Nyquist mindestens 100 Messwerte erforderlich). HAC(100) ist das Leistungsspektrum bezüglich der Messwerte 1 bis 100, HAC(101) das Leistungsspektrum bezüglich der Messwerte 2 bis 101, etc. Pos(100) ist aus HAC(100) abgeleitet, Pos(101) aus HAC(101) etc. Dieses abgeleitete HAC soll erhöht werden, also beispielsweise: ab einem Zeitpunkt bzw. dem entsprechenden Messpunkt soll gelten: Post, mit Einfluss) > 2*Pos(t, ohne Einfluss) . Diese Anforderung bestimmt alle Soll-Verläufe des HAC.Suitable HAC is thus a sequence of squared absolute amounts of Fourier coefficients that exceed Fast Fourier Transform is calculated, for example at the frequencies from 1 to 50 Hertz. Derived HAC is

(Assumption: only integer frequencies, Fourier window, for example 500 milliseconds, f _i absolute amount of the Fourier coefficient of the i-Hertz mode. According to Nyquist, at least 100 measured values are required to cover the frequencies from 1 to 50 Hz). HAC (100) is the range of services with regard to the measured values 1 to 100, HAC (101) is the range of services with regard to the measured values 2 to 101, etc. Pos (100) is derived from HAC (100), Pos (101) from HAC (101) etc. This derived HAC should be increased, for example: from a point in time or the corresponding measuring point the following should apply: Post, with influence)> 2 * Pos (t, without influence). This requirement determines all target courses of the HAC.

D 4000 (minimum equipment requirements):

Surface EEG with one electrode left front, a reference electrode e.g. on the ear (see [16]), an extracranial Coil (as a transmitter) left front.

With 100 readings per half second (see page 500), the minimum sampling rate is 200 / sec.

D 2100 (target course of HAC):

Post, with influence)> 2 * Pos (t, without influence)

D 2200 (HAC calculation rules):

Using Fast Fourier Transform, determine every squared absolute value f _{i of} the Fourier coefficient of the i-th mode i between 1 and 50 Hertz.

D 1000 (Generic Brain Activity Model):

Simplified Wilson-Cowan model (derived from [1], with refractory times set to zero, line delays, and much more), where brain activity is based on the activity of neural oscillators. A neural oscillator does not oscillate or oscillate, depending on its input, as well as non-observable physiological parameters. A neural oscillator is an ensemble of feedback loops consisting of exciting and inhibitory single neurons (in [1] of the order of 10 ⁵ individual neurons, whereby multiples thereof are also permitted, provided the respective neuron ensemble can be described by the following equations, modeled by a system of two nonlinear differential equations :

eine sigmoide Funktion. „x", „y" sind elektromagnetische Observable (bspw. gegenüber einer Referenzelektrode ableitbare Potentiale), „x" für die Gesamtheit der erregenden Einzelneuronen, „y" für die der hemmenden (Gesamtheit = Gesamtheit innerhalb des neuralen Oszillators). „a", „b", „c", „d", sind langsame Nichtobservable. „a" stellt den Einfluß der Gesamtheit der erregenden Einzelneuronen auf sich selbst dar, analog „d" für die Gesamtheit der hemmenden Einzelneuronen. „b" stellt den Einfluß der Gesamtheit der hemmenden Einzelneuronen auf die Gesamtheit der erregenden Einzelneuronen dar, „c" stellt den Einfluß der Gesamtheit der erregenden Einzelneuronen auf die Gesamtheit der hemmenden Einzelneuronen dar. „ρ_x" stellt den endogenen Input von außerhalb des neuralen Oszillators in die Gesamtheit der erregenden Einzelneuronen dar. „ρ_y" stellt den endogenen Input von außerhalb des neuralen Oszillators in die Gesamtheit der hemmenden Einzelneuronen dar. Inputvariable werden als innerhalb des Modells nicht notwendigerweise als langsam angenommen, auf diesen Aspekt wird bei der Erläuterung translokaler Kalibrierung genau eingegangen. Alle Zeiten werden in Millisekunden ausgedrückt, alle anderen Größen sind reine Zahlen. Das τ auf der linken Seite der vereinfachten Wilson-Cowan-Gleichungen gibt die Membranzeitkonstante in Millisekunden an, eine weitere Nichtobservable.Here is

a sigmoid function. "X", "y" are electromagnetic observables (for example, potentials that can be derived from a reference electrode), "x" for the entirety of the exciting individual neurons, "y" for those that inhibit (entirety = entirety within the neural oscillator). "A", "b", "c", "d" are slow non-observable. "A" represents the influence of the entirety of the exciting individual neurons on itself, analogously to "d" for the entirety of the inhibitory individual neurons. "B" represents the influence of the entirety of the inhibiting individual neurons on the entirety of the exciting individual neurons, "c" represents the influence of the entirety of the exciting individual neurons on the entirety of the inhibitory individual neurons. "Ρ _x " represents the endogenous In put from outside the neural oscillator into the entirety of the exciting individual neurons. "ρ _y " represents the endogenous input from outside the neural oscillator into the entirety of the inhibiting single neurons. Input variables are not necessarily assumed to be slow within the model on this aspect is detailed in the explanation of translocal calibration. All times are expressed in milliseconds, all other quantities are pure numbers. The τ on the left of the simplified Wilson-Cowan equations indicates the membrane time constant in milliseconds, another non-observable.

To include external influences, the simplified Wilson-Cowan model is expanded by splitting p: Direct electromagnetic external influences are understood as part of the input: ρ _x = ρ _{x, exo} + ρ _{x, exo} and ρ _Y = ρ _{y, endo} + ρ _{y, exo} , where ρ _exo = Ü, (transmitter signal) denotes the exogenous input into the neural oscillator under consideration based on the signal emitted by the i-th transmitter, and "endo" stands for "endogenous". Ü is a translation operator that depends, among other things, on the distance of the neural oscillator under consideration from the transmitter in question, on the alignment of the individual neurons with respect to the transmitter axis, and on other physiological parameters, as well as on the type of signal sent. Translation operators are not observable.

It is known that the above Nonobservables between different people by factors of are up to 100 different, and even within different Areas of a brain can be very different in vivo, and moreover (can fluctuate slowly compared to EM oscillations). The resulting variety theoretically and practically possible Fashion is therefore almost arbitrary. Controlled local modification Without local calibration, it is therefore normally not possible, on the other hand is translocal under the assumption of relatively stable endogenous inputs Calibration unnecessary.

S 800 (specifying the Minimum calibration requirements):

τ, a, b, c, d, ρ _x , ρ _Y , translation operators for a sensor-transmitter pair.

Only local calibration is required.

D 1100 (minimum calibration requirements):

Local calibration of τ, a, b, c, d, ρ _x , ρ _Y , translation operators for a pair of sensors and transmitters.

E 100 (calibration required?):

Local calibration is required.

D 4100 (equipment restrictions):

Hangs on the equipment used, for example on the input side ordinary EEG adhesive electrodes, Digitization with a sampling rate of 200 / sec. On the output side for example a coil with a maximum magnetic field of 0.5 Tesla. The limitation on a transmitter it brings with it that different fashions not independent can be influenced by each other.

D 4200 (Health Limits for exogenous electromagnetic fields):

For example, includes the recommendations from [14].

S 2000 (calibration):

Is passed through the flowcharts ( 21 - 24 ) detailed.

D 1200 (specific brain activity model):

D 1000 with τ, a, b, c, d, ρ _{x, endo} , ρ _{Y, endo} , as well as D 1300, determined empirically for each relevant brain activity of the respective user in the relevant time frame.

D 1300 (quantification the influence of exogenous EM fields on brain activity):

The influence of exogenous EM fields on the relevant brain activity determined empirically for each relevant brain activity of the respective user in the relevant time frame, represented by control variables ρ _{x, exo} , ρ _{Y, exo} , in the simplified Wilson-Cowan equations for this brain activity.

S 3000 (control or regulation of brain activity with the help of targeted change of control variables and measurement / calculation of HAC):

Is passed through the relevant flowchart ( 25 ) specified.

21 :

D 1100 (minimum calibration requirements):

As previously.

D 1110 (type of calibration):

Local.

D 1120 (scope of calibration):

Sensor S ₁ , transmitter T ₁ .

E 110 (calibration?):

Yes.

S 2100 (local calibration):

Is passed through the flowcharts ( 22 - 23 ) detailed. In the present example, there is only one pair of sensors and transmitters for which calibration is required.

S 2300 (Identify identical Brain Activity):

Not applicable because different sensors and transmitters are not available in this example.

E 120 (translocal calibration?):

No.

D 1200 (specific brain activity model):

As previously.

D 1300 (quantification the influence of exogenous EM fields on brain activity):

As previously.

S 3000:

Please refer 25 ,

22 :

S 2110 (measuring EM brain activity with S ₁ ):

With the help of the left frontal electrode and the reference electrode, potential differences are recorded, and Digitized at 200 values per second. The resulting time series (D 2205) is saved.

D 2205 (empirical time series for S ₁ ):

The time series from S 2110 of potential differences.

S 2120 (calculating actual courses of HAC from the time series of S ₁ ):

With a sampling rate of 200 / sec. receives frequencies between 1 and 50 Hz. Via FFT (Fast Fourier Transformation) receives one for a window of fixed length (100 measuring points) the range of services. This is used for the calculation of pos according to formula in S 500. (S 2130 already starts at HAC (100).)

D 2210 (actual trends of HAC for S ₁ ):

Actual development of the range of services, HAC (100), HAC (101), HAC (102), etc. ie. Pos (100), Pos (101), Pos (102), etc.

D 1000 (generic brain model):

See before.

D 1049 (assumption of influence):

More precisely explained in D 1050, 23 ,

D 1051 (database):

The infinite number in generic Possible brain model Combinations of parameters can be managed a priori many reduced: first, by calculating the limits of the parameter range, in which endogenous inputs exist that lead to a non-constant Lead EM output, secondly, by discretizing an n-fold of this range (Define of a parameter grid, n natural Number). Every parameter set is contained in this grid. All endogenous inputs are considered here (the simplest case) to be constant. As Initial values for the calculation of the voltage variables x and y in the simplified Wilson-Cowan equations become evenly distributed random variables in the interval [–0.5,0.5] selected.

It will now be presented as an example (with exogenous input initially set to zero), which effects different characteristics of the parameters have with the same (constant) endogenous input on the performance spectrum of the resulting mode (respective x-axis: frequency in Hertz, respective y-axis: squared absolute value of the associated Fourier coefficient):
7 shows the HAC of modes by varying the membrane constant τ:
(i) is generated by τ = 5, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _Y = 0.
(ii) is generated by τ = 10, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _Y = 0.
(iii) is generated by τ = 15, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _Y = 0.
(iv) is generated by τ = 20, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.

These modes are under constant Input stationary, so that the HAC shown, plotted over time, also the HAC course is. Transients lasting a few milliseconds, from the initial values into the oscillation, were basically omitted.

8th shows the HAC of modes by varying the self-excitation parameter a:
(i) is generated by τ = 10, a = 14, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(ii) is generated by τ = 5, a = 18, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(iii) is generated by τ = 10, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(iv) is generated by τ = 10, a = 26, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.

Missing ones are clearly recognizable Oscillation in the two smaller self-excitation values.

9 shows the HAC of modes by varying the foreign inhibition parameter b:
(i) is generated by τ = 10, a = 22, b = 15, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(ii) is generated by τ = 10, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(iii) is generated by τ = 10, a = 22, b = 25, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(iv) is generated by τ = 10, a = 22, b = 30, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.

10 shows the HAC of modes by varying the external excitation parameter c:
(i) is generated by ρ _× = 1.5, a = 22, b = 20, c = 10, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(ii) is generated by τ = 10, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(iii) is generated by τ = 10, a = 22, b = 20, c = 18, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(iv) is generated by τ = 10, a = 22, b = 20, c = 22, d = 3, at ρ _x = 1.5 and ρ _y = 0.

Missing ones are clearly recognizable Oscillation with strong external excitation of the inhibitory neurons within of the ensemble (case iv).

11 shows the HAC of modes by varying the self-locking parameter d:
(i) is generated by τ = 10, a = 22, b = 20, c = 14, d = 0, at ρ _x = 1.5 and ρ _y = 0.
(ii) is generated by τ = 10, a = 22, b = 20, c = 14, d = 3, at ρ _x = 1.5 and ρ _y = 0.
(iii) is generated by τ = 10, a = 22, b = 20, c = 14, d = 6, at ρ _x = 1.5 and ρ _y = 0.
(iv) is generated by τ = 10, a = 22, b = 20, c = 14, d = 9, at ρ _x = 1.5 and ρ _y = 0.

Missing ones are clearly recognizable Oscillation with strong self-locking (case iv).

S 2115 (for everyone Set of non-observables: calculate the associated model mode and / or find it in the database):

Insert into the simplified Wilson-Cowan equations and numerical solving the same.

S 2120 (calculating HAC curves from the model modes and / or finding them in the database):

HAC calculation on the results apply from S 2115.

D 1030 (HAC model modes):

Results of S 2120, excerpts shown under D 1051.

D 1040 (HAC measurement invariants and assumptions):

To model modes in empirical fashions to be able to recognize in the simplest case, HAC are chosen, all or part across from the chosen one Measurement method are invariant, or for which this invariance is assumed can be. In the present example (range of services) this is Assumption: the slowdown between the carrier a model mode and the sensor is frequency independent. The is called, The relationship the coefficient of a model mode remains on the way to the sensor receive. As a further assumption, the signal is considered piecewise stationary viewed (with stationarity on average significantly longer than the length of the Fourier window). Further assumption (see [1]) that the potential differences of the arousal and that of the inhibitory neurons in equal parts in the EEG signal come in as x - y.

S 2130 (for S ₁ : decomposition of actual curves of model-related HAC in HAC model modes, with relevance check):

Basically, the EM variables that are described in the brain activity model (e.g. potential differences) are preset as model-related HAC. In the present example (quasi-stationary neural oscillators) the power spectrum is used instead. The a priori change in the default setting depending on the brain activity model used is left to the person skilled in the art. Here: Finding model modes whose HAC model mode, in the simplest case divided by a weakening factor, best explains the range of services with reference to a standard (e.g. L ₁ ). Remove the HAC model mode / attenuation factor from the power spectrum. Finding other model modes whose HAC model mode best explains the new range of services with regard to the standard, etc., until a predefined threshold value is undershot.

For example, if the actual power spectrum modulo noise stationary has maxima at 10 Hertz with amount 1, at 20 Hertz with amount 0.5, at 30 Hertz with amount 0.2, and smaller maxima with further multiples of 10 Hz, then both explain in 12 HAC model modes shown, the actual performance spectrum is approximately equally good, the first (E _1.1 ) with a weakening of approx. 2.2, the second (E _1.2 ) with a weakening of approx. 5.3. Removing, for example, the first HAC model mode / 2.2 provides approximately the zero spectrum. Analog for the second HAC model fashion / 5.3. That is, in the example shown, only the HAC model modes E _1.1 and E _1.2 can be determined.

D 2300 (list of relevant HAC model modes in the measuring range of each sensor S _i ):

Here consists only of E _1.1 and E _1.2 , both for sensor S ₁ .

D 2350 (list of possible Model modes for each element of D 2300):

This list includes, among others, the model fashions M _1,1,1 (E _1.1) and M _1,2,1 (E _1.2) whose production is covered below.

D 2400 (list of possible parameter sets with endogenous input for every model fashion in D 2350):

This list contains, among others, parameter sets for each model mode from D 2350 model modes and endogenous inputs that generate this model fashion according to the simplified Wilson-Cowan equations. D 2400 here includes, for example, for M _1.1.1 the parameter set τ = 10, a = 22, b = 20, c = 14, d = 3, with endogenous input ρ _x = 1.5 and ρ _y = 0, as well as for M _1,2,1 the parameter set τ = 10, a = 26, b = 20, c = 18, d = 3 with endogenous input ρ _x = 1.4 and ρ _y = –0.1.

23 :

D 2300, D 2350, D 2400, D 1000, D 1051, D 4100, D 4200:

As previously.

D 1050 (invariant influences and assumptions):

The control variables in the equations of the generic brain model result from the application of the (still unknown) translation operator Ü belonging to a model mode and the transmitter T ₁ used to a transmitter signal. In the present example: τẋ = –x + S (ax - by + ρ x, endo + Ü x (Transmitter Signal)) τẏ = –y + S (cx - dy + ρ y, endo + Ü y (Transmitter Signal)) ,

(Verifiable) assumptions are made regarding the shape of the translation operator, eg for a special case of acoustic stimulation Ü _X = Ü _y (see [18]). The same assumption is made here, in addition the assumption that, on average, within a scope, two transmitter signals differing only in terms of their amplitude (e.g. amplitude ratio 1 : r, r real number> 0) also behave like 1: r in the amplitude of their associated exogenous inputs. Further assumptions are: ρ _{x, endo} , ρ _{y, endo} stationary during the local calibration.

S 2160 (calculating the test signal):

Each comes first as a test signal any, within the limits of the equipment (D 4100) and health limits (D 4200) realizable signal in question.

The task of the test signal is firstly from D 2400 candidates for generating parameters and endogenous inputs of the model modes of the observed Eliminate HAC model modes until their assignment to respective observed HAC model fashion is unique, and second the translation of the test signal in exogenous input. From the crowd of all potential Test signals can Test signals that meet these conditions can be determined numerically (e.g. choice of ordered function bases plus brute force calculation).

A permissible simplification is to require a test signal to relate both to the measured brain activity and the brain activity model used, and also in a self-consistent manner to a signal to be used in the modification in S 3000. In the case of linear oscillations, a distinction is made between frequencies incommensurable to the oscillation frequency and commensurable frequencies. In the present example, an analogous procedure is used for the fundamental frequency of the oscillation (smallest frequency contained in the power spectrum that can be distinguished from the frequencies of a random signal): 12 the fundamental frequency of the two similar HAC model modes is 10 Hz. Sinusoidal signals of the frequencies 3 Hz, 7 Hz, 9 Hz, 11 Hz etc. can be used as incommensurate test signals. As commensurable test signals, sinusoidal signals of the frequencies 2 Hz, 4 Hz, 5 Hz, 6 Hz, 8 Hz, 10 Hz etc. can be used. (In the present example, phase shifts are irrelevant for the performance spectrum of a mode on a carrier.) Furthermore, elementary loops can basically be used for test signals, ie time-delayed feedback signals of a measurement signal, for example of the form exo (t) = x (t - 10) - y (t - 10).

In addition to the options shown The shape of the test signal is accompanied by various realizable amplitude multipliers, starting with a small amplitude. Example: Test signal = (0.05 Tesla, sin (2 * Pi * t * 3/1000), (1,2,3,4)), t time in milliseconds.

S 2170 (sending the test signal):

Takes place in the order specified in D 2500, with pauses between two transmissions in on the order of multiples of the membrane time constant to avoid temporal summation effects.

S 2110, D 2520:

As previously. If measurement and sending of the test signal take place simultaneously, and for example missing or insufficient sensor shielding leads to artifacts, so these are to be calculated out of the signal. Calculating the strength of the Artifacts generated by a transmitter in a sensor usually take place via the Distance between the two, as well as material equations between two substances found. There is also the possibility measure these artifacts directly before starting a calibration, by natural activities signals of very low amplitude that can be clearly delineated (see above) low that an influence on neural activity is assumed to be unlikely may be). Possible artifacts (e.g. through Muscle movements during measurement) are eliminated conventionally.

S 2120:

As previously.

S 2135 (decomposition after relevant HAC model modes with test signal):

In contrast to S 2130 here not broken down into any HAC model fashions, but into HAC model fashions with test signal.

Here are only parameter sets and endogenous input from D 2400 approved, now through exogenous Input to other solutions if necessary (Model modes with test signal), and thus possibly other HAC model modes lead with test signal.

The weakening of S 2130 remains obtained if it is assumed that the location of the carrier nothing changes in the respective fashion through exogenous input.

The HAC model modes to be considered have, for example, with sinusoidal exogenous input of the frequency 7 Hertz die for 12 (i) in 13 shown shape:
(i) for ρ _exo = 2 * sin (2πt * 7/1000)
(ii) for ρ _exo = 4 * sin (2πt * 7/1000)
(iii) for for ρ _exo = 6 * sin (2πt * 7/1000)
(iv) for for ρ _exo = 8 * sin (2πt * 7/1000)

This already results in Illustration of quantifying the influence of the signal some Observations: That regarding the 7 Hertz and the 14 Hertz component normalized inverted power spectrum doubles the multiplier from 2 to 4. The squared Absolute amount of the 7 Hertz portion indicates when increasing the Multipliers from 2 to 4 the following (approximate) ratio to: 1/4, from 4 to 6: 3/2, from 6 to 8: 4/3.

The same exogenous input generates in the case of the other HAC model modes (ü aus 12 ) in the 14 Performance spectra shown:
(i) for ρ _exo = 2 * sin (2πt * 7/1000)
(ii) for ρ _exo = 4 * sin (2πt * 7/1000)
(iii) for for ρ _exo = 6 * sin (2πt * 7/1000)
(iv) for for ρ _exo = 8 * sin (2πt * 7/1000) In contrast to 13 there is no inversion of the first two components here, the value of the 7 Hertz component is between 3 to 5 times the value of the 14 Hertz component. The changes in the value of the 7 Hertz component when changing the multiplier are here from 2 to 4 (approximately) 10/7, from 4 to 6: 7/5, from 6 to 8: 5/4.

The HAC model modes to be taken into account have, for example, with sinusoidal exogenous input of the frequency 10 Hertz 12 (i) in 15 shown shape:
(i) for _exo = 2 * sin (2πt * 10/1000)
(ii) for ρ _exo = 4 * sin (2πt * 10/1000)
(iii) for for ρ _exo = 6 * sin (2πt * 10/1000)
(iv) for for ρ _exo = 8 * sin (2πt * 10/1000)

Analogous to before there are changes the value of the 7 Hertz component when changing the multiplier from 2 to 4 (approximate) from 6/15, from 4 to 6 from 3/2, from 6 to 8 from 5/4.

The same exogenous input generates in the case of the other HAC model modes (ü aus 12 ) in the 16 Performance spectra shown:
(i) for _exo = 2 * sin (2πt * 10/1000)
(ii) for ρ _exo = 4 * sin (2πt * 10/1000)
(iii) for for ρ _exo = 6 * sin (2πt * 10/1000)
(iv) for for ρ _exo = 8 * sin (2πt * 10/1000)

Similar to the range of services in 14 here too the value of the 7 Hertz component lies between 3.5 and 6 times the value of the 14 Hertz component.

As an example of an elementary loop, a virtual neuron is first introduced, the behavior of which is described by a function u (t). u (t) is the solution of a difference-differential equation similar to Wilson-Cowan, into which the measurement signal x (t) –y (t) delayed by 10 milliseconds is injected: u (t) = - u (t) + S (4 * u (t) + 1.5–2 * (x (t - 10) - y (t - 10)), for the description of the sizes, see above. The numerical solution of this equation is done using linear spline approximation (see, for example, ).

In order to deal with the case of sensors and transmitters that are not active at the same time (relevant, for example, when the sensor is blocked by transmitter signals), measurement and transmission alternate at intervals of 50 milliseconds: that is, the equation above does not couple 2 * (x (t - 10) - y (t - 10)), but 2 * (x (t - 10) - y (t - 10)) * (UnitStep (–sin (0.001 + 2 * Pi * t * 0.02)), and into the simplified Wilson-Cowan equations not ρ _{x, exo} (t) = ρ _{y, exo} (t) = multiplier * u (t), but ρ _{x, exo} (t) = ρ _{y, exo} (t) = multiplier * u (t) * UnitStep (sin (0.001 + 2 * Pi * t * 0.02)) where UnitStep represents the Heaviside function.

This results in the for 12 (i) in 17 shown shape:
(i) for multiplier = 4;
(ii) for multiplier = 8;
(iii) for multiplier = 16;
(iv) for multiplier = 32;

Using the same elementary loop results in for 12 (ii) the in 18 shown shape:
(i) for multiplier = 4;
(ii) for multiplier = 8;
(iii) for multiplier = 16;
(iv) for multiplier = 32.

It can be seen in the 17 and 18 the result of a significantly wider range of multipliers. This is in contrast to the continuous signals of the 13 - 16 usually based on more complex mode separation for alternating measurement and transmission. The non-linearity of the effects when the multiplier is increased is particularly evident in the transition from (iii) to (iv) in 17 clearly, as well as the separability of the 17 and the in 18 underlying modes with corresponding exogenous input - in contrast to mere observation (see 12 ).

For example, the changed HAC model mode should be at a 0.4 Tesla 7 Hertz sinusoidal test signal with regard to the defined L ₁ standard is best described in 13 (i) HAC model mode shown can be approximated with a test signal.

(No phase considerations in this simple example. With a fine-tuning calibration additionally Sequences of phase-shifted test signals are used.)

Expressed in parameter sets, endogenous and exogenous inputs, the result is as follows:
There is a model mode with nonobservables τ = 10, a = 22, b = 20, c = 14, d = 3, at ρ _{x, endo} = 1.5 and ρ _{y, endo} = 0, into which a 0.4 Tesla 7 Hertz sinusoidal signal of the transmitter T ₁ as 2 * sin (2 * Pi * t * 7/1000 + phase), and which is subject to an attenuation of 2.2 on the way from its carrier to the sensor S ₁ .

Note 1: In general is not necessarily assume that suitable modes by parameters and inputs of the selected Lattices are generated, i.e. they are local refinements of the lattice and / or Perform interpolations.

Note 2: Inconsistent results within a signal sequence indicate that during the Sending one or more jumps of the quasi-stationary endogenous inputs have taken place. In this case you don't have to only modes with the current endogenous inputs for calibration considered but also those with other potential inputs.

E 210 (several best approximating Sets of Parameters):

Not in this example. explicitness is up to equivalence Are defined. Two sentences of parameters are called equivalent, if the limit cycle of the respective 1-mode is similar to that of all inputs Limit cycle of the respective 2-mode is.

24 :

Not applicable since in this example only local calibration required becomes.

25 :

D 2100, D 1200, D 1300, D 1050, D 1051: As before.

D 1010 (actual HAC profiles):

gemäß S 500.Pos is continuously calculated from the ongoing measurements of brain activity ("Running": = "at suitable time intervals").

according to S 500.

S 2110, S 2120:

As previously.

S 3100 (calculating a or more transmitter signals):

The usually infinite number of possible transmitter signals is reduced analogously to the calculation of the test signals. In the simplest case, transmitter signals are used that have already been used as test signals. The transmitter signals are checked for feasibility (D 4200, D 4100, D 4300) and ordered according to predefined utility functions (e.g. low frequency, minimum field strength, etc.). The best possible transmitter signal is then automatically selected. Example: 0.4 Tesla 7 Hertz sinusoidal signal from the transmitter T ₁ . The consequences arise under otherwise identical conditions as: HAC model fashion in 12 (i) through the in 13 (i) replace. Pos (12i) = 0.9, Pos (13i)> 10 ⁵ clearly fulfills the behavior goal Pos (with influence)> 2 * Pos (without influence). If the 7 Hertz component is not to be amplified for reasons of possible interaction with other brain activities (see example 3), an elementary loop with multiplier 32 (see FIG. 17 iv) to achieve the behavioral goal.

S 3400 (send):

Send the transmitter signal (s) selected according to S 3100 (here: with the transmitter T ₁ ).

D 4040 (HAC prognosis):

The model modes with exogenous inputs determine the corresponding brain activity over time, and provide a time series, from the parts of which lie in the future, a theoretical HAC time series is calculated as before, the latter being the HAC prognosis. In the present example, only one model mode is relevant, the HAC prognosis is therefore obtained by dividing the in 13 (i) represented HAC by the attenuation factor 2.2.

S 3500 (compare):

For one thing, the following applies: subtraction the actual HAC history from S 2120 and the forecast HAC history (D 4040), amount calculation for any frequency between 1 and 50 Hertz (deviation = maximum of these amounts), at others will achieve the behavioral goal (the target HAC course) determined analogously by comparing the actual and sol HAC curve.

E 300 (deviation acceptable?):

Make the assumptions and the results calibration, it can be assumed that forecasting and the actual HAC differ only insignificantly, (deviation <predefined Threshold) one can therefore continue with the transmission (p 3400). If this is not the case, the numerical solution applies to Equations of the brain model with other endogenous inputs (S 3600) to consider, to what extent jumps of the piecemeal stationary endogenous inputs to explain the deviation.

In case the The transition takes place within the calculated time frame to S 3800, otherwise the message "behavioral goal reached".

S 3600 (change calculation)

Occasional constant endogenous inputs to the simplified Wilson-Cowan equations.

E 310 (consistent?):

If the change can be explained by changed endogenous inputs, the calibration is changed accordingly (ρ _{x, endo} : = new value, ρ _{y, endo} : = new value ') and return to S 3100.

Let yourself the change not from changed explain endogenous inputs, Cancel sending, back to S 2000. For the unlikely event of multiple returns to S 2000: cancel application.

In conclusion to this example to notice that the behavioral goal was controlled by calibration and subsequent Modification reliable is achieved.

Example 2:

20 :

D 2000 (behavioral goal):

Enhance positive emotions and weaken negative emotions for the duration of application

D 3000 (behavior model):

Davidson model (see e.g. [11]), after which positive emotions with a higher quotient from beta (13-30 Hz) - EEG activity to alpha (8-12 Hz) - EEG activity in the left front Cortex go hand in hand, and negative emotions with a higher quotient from Beta (13-30 Hz) - EEG activity to alpha (8-12 Hz) - EEG activity in the right front Cortex.

S 500 (specify HAC. Specify Target curves from HAC):

für einen linksfrontalen Sensor S₁ und

für einen rechtsfrontalen Sensor S₂. (f_ij Koeffizient der i-Hertz-Mode des Sensors j). Pos soll erhöht werden, also beispielsweise Pos(mit Einfluss) > 2*Pos(ohne Einfluss), Neg soll verringert werden, also beispielsweise Neg(mit Einfluss) < 0.5*Neg(ohne Einfluss). Diese Anforderung definieren die Soll-HAC Verläufe. Berechnung wie in Beispiel 1.Analogous to example 1. Derived HAC are

for a left-front sensor S ₁ and

for a right-front sensor S ₂ . (f _ij coefficient of the i-Hertz mode of the sensor j). Pos should be increased, e.g. Pos (with influence)> 2 * Pos (without influence), Neg should be reduced, e.g. Neg (with influence) <0.5 * Neg (without influence). This requirement defines the target HAC courses. Calculation as in example 1.

D 4000 (minimum equipment requirements):

Surface EEG with an electrode S ₁ on the left front with a reference electrode on the left ear, an electrode S ₂ on the right front with a reference electrode on the right ear (see [16]), two extracranial coils (transmitters T ₁ and T ₂ ), where T _1, for example a connecting line running on the head surface between S ₁ and a central electrode Cz (see [16]) is located in the immediate vicinity of S ₁ , and T ₂ analogously between S ₂ and Cz, the distance between S ₁ and T _{1 being the} same is the distance between S ₂ and T ₂ . Everything else is as in example 1.

D 2100 (target courses of HAC):

Pos (with influence)> 2 * Pos (without influence) and Neg (with influence) <0.5 * Neg (without influence)

D 2200 (HAC calculation rules):

.As in example 1, additionally for the second, right-frontal sensor

,

D 1000 (Generic Brain Activity Model):

Simplified Wilson-Cowan model, as in example 1.

S 800 (specifying the Minimum calibration requirements):

τ, a, b, c, d, ρ _X , ρ _Y , translation operators for the four sensor-transmitter pairs S ₁ -T ₁ , S ₁ -T ₂ , S ₂ -T ₁ , S ₂ -T ₂ . Again, local calibration is sufficient, since both frontal brain areas have no direct physiological connections and are simply assumed to be independent.

D 1100 (minimum calibration requirements):

Local calibration of τ, a, b, c, d, ρ _X , ρ _Y , the translation operators for the four sensor-transmitter pairs S ₁ -T ₁ , S ₁ -T ₂ , S ₂ -T ₁ , S ₂ - T ₂ .

E 100 (calibration required?):

Local calibration is required.

D 4100 (equipment restrictions):

Analogous to example 1, on the output side for example two coils, each with a maximum magnetic field of 0.5 tesla.

D 4200 (Health Limits for exogenous EM fields):

As in example 1.

S 2000 (calibration):

See later.

D 1200 (specific brain activity model):

As in example 1.

D 1300 (quantification the influence of exogenous EM fields on brain activity):

As in example 1.

S 3000 (control or regulation of brain activity with the help of targeted change of control variables and measurement / calculation of HAC):

Is passed through the relevant flowchart ( 25 ) specified.

21 :

D 1100 (minimum calibration requirements):

As previously.

D 1110 (type of calibration):

Local.

D 1120 (scope of calibration):

Sensors S ₁ , S ₂ , transmitters T ₁ , T ₂ .

E 110 (no calibration?):

Yes.

S 2100 (local calibration):

Analogous to example 1, for the four sensor transmitter pairs S ₁ -T ₁ , S ₁ -T ₂ , S ₂ -T ₂ , S ₂ -T ₁ . A significant simplification when running the local calibration four times is that parameters and endogenous inputs of modes that can be determined by S _{1 are} already determined when the local calibration is carried out for the pair S ₁ -T ₁ , and thus during the local calibration for S ₁ -T _{2 can be taken} as given (among other things in the decomposition) Analog for modes in the measuring range from S ₂ . It is recommended to start the local calibration of a sensor with the transmitter that has a minimal spatial distance compared to this sensor.

The result of the local calibration is, for example:
For sensor S ₁ :
A HAC model mode with underlying parameters τ = 10, a = 22, b = 20, c = 14, d = 3, with endogenous input ρ _x = 1.5 and ρ _Y = 0, shown in 12 (i) , which is affected by a 0.2 Tesla 7 Hertz signal from the transmitter T ₁ as endogenous input ρ _exo = 2 * sin (2πt * 7/1000) (see 13i ), and a 0.4 Tesla 10 Hertz signal from transmitter T ₂ as endogenous input ρ _exo = 2 * sin (2πt * 10/1000) (see 15i ).

For sensor S ₂ :
A HAC model mode with underlying parameters τ = 10, a = 26, b = 20, c = 18, d = 3 with endogenous input ρ _x = 1.4 and ρ _y = –0.1, shown in 12 (ii) on which a 0.2 Tesla 7 Hertz signal of the transmitter T _{1 is} not noticeable (see 12ii ), and a 0.4 Tesla 10 Hertz signal from the transmitter T ₂ as endogenous input ρ _exo = 4 * sin (2πt * 10/1000) (see 16II ).

S 2300 (Identifying identical brain activities):

The above fashions are not identical.

E 120 (translocal calibration?):

No.

D 1200 (specific brain activity model):

As previously.

D 1300 (quantifying the Influence of exogenous electromagnetic fields on brain activity):

as before.

22-23 :

As before, or as just below S 2100 shown.

24 :

Not applicable because only local calibration needed becomes.

25 :

Analogous to Example 1, except for S 3100, which will be explained in more detail below: For example a 0.2 Tesla 7 Hertz signal of the transmitter T _{1 is combined} with a 0.4 Tesla 10 Hertz signal of the transmitter T ₂ . The left-frontal (sensor S ₁ ) mode receives the exogenous input ρ _exo = 2 * sin (2πt * 7/1000) + 2 * sin (2πt * 10/1000), the right-frontal mode (sensor S ₂ ) the exogenous input ρ _exo = 4 * sin (2πt * 10/1000). Neg (with influence) <0.5 * Neg (without influence) can be omitted directly 16 (ii) seen. Pos (with influence) is calculated as 2.01 using the simplified Wilson-Cowan equations, which have been expanded with exogenous input. The behavioral goal is thus achieved.

Example 3:

Example 3 is not meant to be in the same detail like the previous examples (e.g. all modes, local calibration all sensor-transmitter pairs, etc) explained but mainly in terms of Significant deviations from the previous examples:

20 :

D 2000 (behavioral goal):

Prolong mental awareness for the Duration of application.

D 3000 (behavior model):

(max = 50)Based on, for example, [20] and [21], Fmθ (Frontal midline theta), ie 6–7 HZ activity in the area of the Fz electrode (see [16]) goes hand in hand with the maintenance of focused attention in mental processes. This is referred to as the Ishihara-Yoshii model. Near-surface neurons in the measurement area of the Fz sensor are directly connected to some other areas, in particular subcortical driving was found. Range of services and its calculation as before instead of Pos or Neg this time for each sensor

(max = 50)

D 4000 (minimum equipment requirements)

To determine any preparation phenomena, it makes sense to supplement the Fz electrode with at least one adjacent electrode with a measuring range that is physiologically directly connected to the measuring area of the Fz electrode (for example, the Cz electrode), i.e. surface EEG with two electrodes and two reference electrodes ( as shown in [21]). Two coils as transmitters are located extra-cranially in the immediate vicinity of the relevant electrode: T ₁ anterior with respect to Fz, T ₂ posterior with respect to Cz. Everything else as in example 1.

D 2100 (target courses of HAC):

For example: Att ₁ (with influence) = Att ₁ (without influence, with full driving), Att ₂ (with influence) <= Att ₂ (without influence). Here, "1" stands for "Fz and reference electrode", "2" for Cz and reference electrode "," = "stands for" the right side does not deviate more than 20% from the left side ".

D 2200 (HAC calculation regulations): range of services as in example 1, additionally for each of the two sensors

D 1000 (Generic Brain Activity Model):

. Hierbei stellt „h_ji" die Stärke des Einflusses des I-ten auf den j-ten neuralen Oszillator dar, und das betreffende Δ die Verzögerung des Wirksamwerdens dieses Einflusses.Simplified Wilson-Cowan model, as in example 1, whereby the role of translocal effects is now considered more closely and the assumption of constancy for endogenous input is abandoned. In the simplest case of translocal calibration, the endogenous input into the entirety of the exciting individual neurons of the jth oscillator can be applied to the old in

, Here, "h _ji " represents the strength of the influence of the ith on the jth neural oscillator, and the Δ in question represents the delay in the effect of this influence.

S 800 (specifying the Minimum calibration requirements):

As in example 2, plus functional matrix. Local and translocal calibration required.

D 1100 (minimum calibration requirements):

As in example 2, plus functional matrix.

E 100 (calibration required?):

Yes.

D 4100 (equipment restrictions):

Analogous to example 2.

D 4200 (Health Limits for exogenous electromagnetic fields):

As in example 2.

S 2000 (calibration):

Is passed through the flowcharts ( 21 - 24 ) detailed. It is assumed that the stationarity of the endogenous input is given over the calibration period, but not over the application period.

D 1200 (specific brain activity model):

As in example 1.

D 1300 (quantifying the Influence of exogenous EM fields on brain activity):

As in example 1.

S 3000 (control or regulation of brain activity with the help of targeted change of control variables and measurement / calculation of HAC):

Is passed through the flowchart ( 25 ) specified.

21

D 1100 (minimum calibration requirements):

As previously.

D 1110 (type of calibration):

Translocal (includes local).

D 1120 (scope of calibration):

As in example 2.

S 2100 (local calibration):

Is passed through the flowcharts ( 22 - 23 ) detailed.

S 2300 (Identifying identical brain activities):

Analogous to example 2.

E 120 (translocal calibration?):

Yes

S 2400 (translocal calibration):

Is at 24 represented exactly.

D 1200, D 1300:

As in example 2, plus functional matrix.

22 :

Analogous to Example 2. Of interest, for example with attenuation factor 1 in the measuring range of sensor S _{1, is} a HAC model mode with underlying parameters τ = 10, a = 16, b = 20, c = 8, d = 3, with endogenous inputρ _x = 1.5 + m * sin (2 * π * t * 7/1000) and ρ _Y = 1.5 + m * sin (2 * π * t * 7/1000), shown for m = 0 in 19 (i) , for m = 0.2 in 19 (ii) , and for m = 0.4 in 19 (iii) , (In this example it is assumed that subcortical driving with 7 Hertz affects the excitatory and the inhibitory part of the described neuron ensemble equally.) For example, a feedback signal from the transmitter T _{1 with a} maximum amplitude of 0.5 Tesla should have an effect on the mode than that under example 1, S 2135 presented elementary loop with a multiplier of 10 act as exogenous input. Analog for T ₂ with a multiplier of 0.

Local calibration here expediently includes phase shift of test signals. The HAC minimum of the driven mode provides the phase shift required to switch the mode off? For example, should a T ₁ 0.5 Tesla be around 7 Hertz? shifted sinusoidal signal takes the form of 0.2 * sin (-2 * Pi * t * 7/1000) as an exogenous input, and consequently switch off the neural oscillator (see result of this) 19i ).

The only S ₂ mode is, for example, the S ₁ mode from Example 1, but with an attenuation factor of 4.4. Both of the above-mentioned T ₁ signals should have no influence on the S ₂ mode, and neither should 0.1 Tesla sine signals from T ₂ on the theta mode.

23 :

Analogous to example 2.

24 :

D 1300 (translation operators):

If i and k are different, these translation operators are, for example, for the two signals (elementary loop or 10 Hz sinusoidal signals with the field strengths mentioned), just like 22 shown, zero.

S 2410 (HAC forecast):

As previously.

For the S ₁ mode: S 2420 (change):

Example: Sending a T ₁ 0.5 Tesla 7 Hertz sine signal shifted by rθ switches the S ₁ mode off.

For the S ₁ mode: S 2430 (determining the S ₂ gap):

The forecast HAC for S ₂ results, since the T ₁ signals have no direct effect on the S ₂ mode, from their unchanged continuation, which should match the S ₂ actual HAC.

For the S ₂ mode: S 2420 (change):

Example: Frequency shifting of the S ₂ mode to a basic frequency of 11 Hertz.

For the S ₂ mode: S 2430 (determining the S ₁ gap):

The forecast HAC for S ₁ results, since the T ₂ signals have no direct effect on the S ₁ mode, from their unchanged continuation, which should correspond to the S ₁ actual HAC. Here, for example, there is a change in 19 (iii) shown signal component in the direction 19 (ii) ,

S 2440 (back calculation on functional matrices explaining gaps):

s No gap in S ₁ change, therefore cell (1,2) is zero. (Mode 1: = the S ₁ mode shown, Mode 2: = the S ₂ mode shown).

Ambiguous gap in cell (2,1).

E 400 (unique?):

Check for more than one functional matrix results from S 2440. If you look at each Not for use any connections between brain activities interested, but only for certain relationships, for example, only between the brain activities i and j, it is sufficient to use the (i, j) th and clearly fill the (j, i) cell of the functional matrix. in the This example is only interested in whether HAC-preserving changes mode 1 is spreading, i.e. Change mode 2, for this it is sufficient to consider the cell (1,2), and this has the value zero.

S 2221 (change signal):

For example generating frequency shifts, Phase shifts, amplitude changes, Change stationarity, noise Add, Change Ljapunov exponents, and much more, which is no longer required in the present example.

D 2410 (functional matrix):

The result includes a functional matrix, which has the following form, for example:

25 :

Analogous to example 2. In the present example, the Fmθ is, for example, when driving has dropped to zero (see 19i ) stabilized with the help of the fed-back T ₁ signal near its HAC course with intact subcortical driving (intact driving see 19iii , no driving, but transmitter signal s. 19iv ), which has no effect on the S ₂ alpha mode at the same time. The behavioral goal has been achieved.

Explanations and notes:

For D 1000:
A brain activity model is a set of difference and / or differential equations, e.g. shape ẋ (t) = f (x, t, local parameters, endogenous input, exogenous input) for a brain activity, x brain activity characteristic, t time. "Exogenous input" refers to the values of input variables in the equations of the brain activity model (artificially generated by extracranially generated electromagnetic fields) (in contrast to "endogenous input", which usually comes about through normal sensory or nerve channels, for example through photic stimulation, through thalamic Pacemaking, by listening to a symphony, and much more). Exogenous input = Ü (transmitter signal), Ü translation operator. The time derivative of x and x itself can be observed. The function f depends on the brain activity model used, and determines the derivative of x using x, t, and non-observable endogenous and non-observable exogenous input for a non-observable parameter set.

Different models of brain activity to lead to different calibration results, with possibly different ones resulting modifications. For appropriate brain activity models the behavioral goal becomes reliable reached.

For S 2000:
The nonlinearity of the system makes it possible to determine the non-observable variables including the translation operator Ü by suitable variation of a transmitter signal with a field strength that is as small as possible (“test signal”).

The dynamic calibration makes a difference differ from existing mathematical methods for parameter estimation (the usually distribution assumptions of those considered as random variables Assume parameters, and by observing the course of trajectories over the Assumption of increasing convergence from the observations and the true parameters deduce the values of the latter, see e.g. [7]), as well as from the engineering sciences adaptive regulations / parameter estimates (these are essentially only for linear time invariant systems can be used, in general you feel yourself as part of the control with very simple functions as manipulated variables via minimization from "ideal Control variable minus actual Controlled variable ", see e.g. [8th]).

Dynamic calibration is based on Difference to the "passive Measurement "(conventional non-invasive in vivo observation) of the mathematical method shown on "active measurement" (transmission of Signals in the system in question, as well as measuring the system in Interaction with the signal). The result of active measurement is multiple in nonlinear deterministic and / or nonlinear stochastic systems (like the human brain) significantly different from those in the Measurement of linear systems usual Pulse or jump responses, or the like (e.g. stochastic Resonance).

In contrast to known adaptive Rules / parameter estimates First, dynamic calibration is usually one Second, regulation to be separated and this preceding process the main focus of dynamic calibration is on the used test functions, their diversity, and thus separability between different sentences of non-observables of the complexity of the system under investigation is appropriate (as opposed to simple test functions such as Heaviside functions or similar, whose System response often over long periods must be observed which is also unreasonable in the in-vivo case), third the type of approximation to a parameter set that explains the measured values is fundamentally different (see calibration research, this reduces subsets of a discretized Variety of parameters until only equivalent parameter sets remain, in contrast to the target-actual comparison of controller variables).

Dynamic calibration also provides additionally to determine the non-observables in the case of heterogeneous and / or complex systems crucial unknown translation operators of parameters external to the system generated fields (e.g. field strength and / or frequency patterns of extracranial magnetic fields) in exogenous Input of the system equations.

For D 2410:
A functional matrix results as the mxm matrix of all m observed different brain activities, the cell m _ij = 0 if no functional connection from the jth activity to the ith activity can be determined. Functional connections between brain activities require physiological connections of the underlying brain components, but the latter connections do not allow any information about the coupling of brain activities. Possible functional matrices are those that, after parameter discretization, provide solutions that are compatible with the observations for the entirety of the observed brain activities, whereby in the equations for a brain activity z _i its endogenous input is replaced by, for example, residual input + sum of all upstream activities z _j * respective influence strength * z _j (t-delay). ("A upstream B" means "change from A changes B", equivalent to: B downstream A.) s

In an advantageous embodiment the calibration becomes n> 0 Test signals aborted ("quick calibration"), a set of parameters and endogenes compatible with the measured values and exogenous inputs provisionally determined as the calibration result. Additional resulting from the modification (S3000) readings are used to adapt the above sentence.

In an advantageous embodiment assumptions of influence and invariants (D1049, D1050) are validated.

In an advantageous embodiment is the EM process with sensory input (acoustic, optical, etc.) combined. Example: Instructions to the user in example 3, a mental task with the start of calibration (see example [20]) perform, making it easier to find suitable Fmθ modes. Other Example: Acoustic pacemaking for translocal calibration of auditory Brain processes.

In an advantageous embodiment, the effect of the method is stabilized beyond the duration of the individual application with the aid of suitable repetition rates to be determined for the individual user via external validation. This effect of suitable repetition rates is, for example, for applications known from TMS (transcranial magnetic stimulation with magnetic fields of 1-2 Tesla) (see for example [3]).

In an advantageous embodiment is operated together with the calibration sensor optimization: switch on previously inactive sensors and / or change in position of sensors and / or alignment change sensors in such a way that they are identified and / or unidentified brain activity particularly well detected by the relevant, possibly realigned sensor become. Conventional optimization methods are used for sensor optimization suitable.

In an advantageous embodiment, together with the calibration, sensor optimization is carried out by interconnecting existing sensors to form virtual sensors, in such a way that brain activities are recorded particularly well by the relevant virtual sensor. Example: S ₁ , S ₂ , S ₃ , S ₄ , via a function f (S ₁ , S ₂ , S ₃ , S ₄ ) = 0.3 * S ₁ + 0.5 * S ₂ + 0.02 * (S ₃ ) ² + 0.5 * sin (S ₁ + S ₄ ) connected to S _virtually . A suitable f is determined for each subset of sensors using conventional optimization methods.

In an advantageous embodiment is operated together with the calibration transmitter optimization: Switch on previously inactive transmitter and / or change of position of transmitters and / or change of orientation of transmitters in such a way that these brain activities especially of the relevant, possibly realigned transmitter be influenced well. Conventional optimization methods are used for transmitter optimization suitable.

In an advantageous embodiment is combined with the calibration transmitter optimization by interconnection existing transmitter operated to virtual transmitters, such that brain activity influenced particularly well by the relevant virtual transmitter become. The optimum (the virtual transmitter) results from the translation operators with the help of conventional Optimization method In an advantageous embodiment of S 2130 will be the decomposition for n sensors (n integer between 1 and the maximum number of sensors used Sensors) carried out simultaneously, ie. the time series to be broken down is an n-tuple of one-sensor time series.

In an advantageous embodiment from S 2130 the decomposition for an n-tuple of one-sensor time series performed with pursuit procedure (e.g. Matching Pursuit, see [13]).

In a further advantageous embodiment from S 2130 the spatiotemporal decomposition of the n-tuple takes place of single-sensor time series, for example with the Karhunen-Loève method (see e.g. [17], [18]) or Independent Component Analysis ("ICA", s. e.g. [22]).

In a further advantageous embodiment S 2130 is decomposed after being embedded in a metaphase space, making stationary and non-stationary Shares are separated (see, for example, [15]).

In an advantageous embodiment S 2130 is decomposed in parallel using several methods, whereby only those (with a priori defined weighting) of one Most of these methods identified modes for further processing (including relevance check) be accepted.

In an advantageous embodiment from D 2000 is the change or no change of perception and / or ability to perceive and / or willingness to perceive set as a behavioral goal. An example of this is the change the ability to discriminate for certain Stimuli or classes of stimuli.

In an advantageous embodiment from D 2000 is the change or no change of actions and / or ability to act and / or willingness to act set as a behavioral goal. An example of this is the improvement of reaction speed.

In an advantageous embodiment from D 2000 is the change or no change of activation and / or activation ability as a behavioral goal set.

In an advantageous embodiment from D 2000 is the change or no change of motivation and / or ability to motivate and / or motivation set as a behavior goal.

In an advantageous embodiment from D 2000 is the change or no change of attention and / or attention ability as a behavioral goal set.

In an advantageous embodiment from D 2000 is the change or no change of memory and / or memory content and / or memory access set as a behavior goal.

In an advantageous embodiment from D 2000 is the change or no change of learning and / or learning ability and / or willingness to learn as a behavioral goal.

In an advantageous embodiment from D 2000 is the change or no change of consciousness set as a behavior goal.

In an advantageous embodiment from D 2000 is the change or no change of emotions and / or emotional ability and / or willingness to emotion as a behavioral goal.

In an advantageous embodiment from D 2000 is the change or no change of appetites and / or aversions as a behavioral goal.

In an advantageous embodiment from D 2000 is the change or no change of thinking and / or thinking ability and / or willingness to think as a behavioral goal. ("Think" is referred to here as "performing mental Processes ".).

In an advantageous embodiment of D 2000, the change or non-change of Ver holding processes as a behavioral goal.

In an advantageous embodiment from D 2000 is the change or no change of behavioral correlations as a behavioral goal.

In an advantageous embodiment D 2000 combines several compatible behavioral goals.

In an advantageous embodiment D 2000 makes several behavior goals that are not necessarily compatible hierarchically combined, ie. it becomes a priority list of behavioral goals and, if necessary, each other contradicting procedural steps to lower priority Behavior goal Process steps later or not executed at all.

In an advantageous embodiment from D 2000 to other behavioral goals that of freedom from seizures with top priority added what an application of [9] to non medically stressed users includes.

In an advantageous embodiment D 3000 will do the job Behavioral model of the extremely extensive Literature taken (e.g. for the presented examples from [11], [20], [21] each dozens of publications with the same / similar Object).

In an advantageous embodiment D 3000 uses several behavior models in parallel, only those control or regulation steps (S 3000) carried out be the (with an a priori defined weighting) with a Most of these models are compatible

In an advantageous embodiment usually with the help of psychological tests external validation behavior change / maintenance for the individual users during and / or after the EM application. This will check which one Scope of the used, usually statistically validated behavior model applies to the user concerned.

In an advantageous embodiment from D 1000 several generic brain activity models are used in parallel, being only those control or Control steps (S 3000) are carried out (with a a-priori defined weighting) with a majority of these models are compatible.

In an advantageous embodiment In the respective steps of the process, individual sensors are used and / or individual transmitters by quantities of sensors or quantities replaced by transmitters. This serves to establish and control Modification of nonlocal spatiotemporal modes, such as in Jirsa hook models occur (see, for example, [18]).

In an advantageous embodiment D 4000 exceeds the minimum equipment requirements, in order to be able to receive additional information that is required for later applications with possibly changed Behavioral goals can be useful (e.g. basic Application of the 10/20 surface EEG, s. e.g. [16]). The advantage of this configuration is possible Standardization of parts of the apparatus as well as some possible ones Use commercially available Hardware.

In a further advantageous embodiment D 4000 exceeds the minimum equipment requirements, for additional control or control options to have (e.g. for every non-reference electrode des 10/20 surfaces EEGs a coil located directly in front of this electrode).]). The advantage of this configuration lies in the possible standardization of Parts of the apparatus.

In an advantageous embodiment of S 3000 are used as the control variable exogenous EM fields through sensory inputs and / or biochemically effective Substances added, whereby basically nothing changes in the method steps shown.

In further advantageous configurations of the procedure are called test and Transmitter signals Signals with stochastic components are used (especially when using brain activity models with explicit stochastic Components such as [25], an extension of [18], with the The aim of harnessing stochastic resonance and coherence Resonance, see for example [26] for the FitzHugh-Nagumo model).

In further advantageous configurations of the procedure are called test and Transmitter signals used signals with chaotic parts (in particular when using brain activity models with explicit chaotic components, such as extensions from [1] with locally strongly coupled oscillators).

In a further advantageous embodiment of the method become inactive modes directly by transmitter signals activated, i.e. through the direct effect of the signals on the Fashion.

In a further advantageous embodiment of the method, modes are indirectly influenced by transmitter signals, i.e. about direct influence on fashions, which in turn (see functional matrix) Has an impact on the modes to be influenced indirectly.

In a further advantageous embodiment of the process, bifurcation points are checked, i.e. it will qualitative changes EM brain activity (as in example 3, where the inactive without driving neural oscillator was turned on).

In a further advantageous embodiment Inactive modes of the method are activated.

In a further advantageous embodiment The method amplifies the amplitude by activating inactive ones Fashions.

In a further advantageous embodiment the procedure is based on age-related flattening (for EEG see for example [12]) counteracting amplitude amplification.

In a further advantageous embodiment In the method, non-stationary signals are decomposed first into a stationary and a non-stationary Part (see, for example, [15]), the test and transmitter signals used then with the non-stationary Proportion to be modulated.

In a further advantageous embodiment The procedure is used to identify users on the Basis of stable calibration data. Already conventionally spectrally decomposed EEG data have for one Individual time-stable components (proven up to five years Duration, for example [10]).

Documents considered essential:

[1] "Excitatory and inhibitory interactions in localized populations of model neurons ", Wilson HR & Cowan JD, Biophysical Journal 1972; 12: 1--22

[2] WO 00/29970 A1

[3] "Therapeutic application of repetitive transcranial magnetic stimulation: a review ", Wassermann EM & Lisanby SH, Clin Neurophysiol 2001; 112: 1367-1377

[4] WO 98/18384 A1

[5] "Anticipation of epileptic seizures from standard EEG recordings ", Le Van Quyen M & Martinerie J & Navarro V & Boon P & D'Havè M & Adam C & Renault B & Varela F & Baulac M, The Lancet 2001 Jan 20; 357: 183-8

[6] WO 01/21067 A1

[7] "Numerical Solution of Stochastic Differential Equations ", Kloeden PE & Platen E, Springer Verlag, Berlin 1992, chapter 6.4

[8] "Control engineering", Föllinger O , Hüthig Buch Verlag, 8th, revised. Ed., Heidelberg 1994 Chapter 1.6

[9] DE 10215115.6

[10] "Intraindividual Specificity and Stability of Human EEG: Comparing a Linear vs a Nonlinear Approach ", Dünki RM & Schmid GB & Stassen HH, Method Inform Med 2000; 39: 78-82

[11] "An EEG Biofeedback Protocol for Affective Disorders ", Rosenfeld JP, Clin Electroencephalography 2000; 31 (1): 7-12

[12] "Guide to EEG Practice", Ebe M & Homma I, Gustav Fischer Verlag, Stuttgart 1992, Chapter 6.2.2

[13] "A Wavelet Tour of Signal Processing ", Mallat S, Academic Press, San Diego 1999, chapter 9.5.2

[14] "Applied Bioelectricity", Reilly JP, Springer Verlag, New York 1998 , Chapter 11

[15] "Stationarity and nonstationarity in time series analysis ", Manuca R & Savit R, Physica D 1996; 99: 134-161

[16] "Guide to EEG Practice", Ebe M & Homma I, Gustav Fischer Verlag, Stuttgart 1992, chapters 3.6.1, and 3.6.2

[17] "Nonlinear Spatio-Temporal Dynamics and Chaos in Semiconductors ", Schöll E, Cambridge University Press, Cambridge 2001, Chapter 7.3.4.

[18] "A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics ", Jirsa VK & Hook H, Physica D 1997; 99: 503-526

[19] "Approaches to verbal, visual and musical creativity by EEG coherence analysis ", Petsche H, Int J Psychophysiol 1996; 24: 145-159

[20] "Frontal midline theta rhythm and mental activity ", Inanaga K, Psychiatry and Clin Neurosciences 1998; 52: 555-566

[21] "Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulate cortex in humans ", Asada H & Fukuda Y & Tsunoda S & Yamaguchi M & Tonoike M, Neuroscience Letters 1999; 274: 29-32

[22] "Imaging Brain Dynamics Using Independent Component Analysis ", Jung TP & Makeig S & McKeown DEFECTIVE (6) & Bell AJ & Lee TW & Sejnowski TJ, Proceedings of the IEEE July 2001; 89 (7): 1107-1122

[23] "Spline approximations for functional differential equations ", Banks HT & Kappel F, J Differential Equations 1979; 34: 496-522

[24] "Mathematical aspects of Hodgkin-Huxley neural theory, "Cronin J, Cambridge University Press, Cambridge 1987, chapters 2 and 3

[25] "Impacts of noise on a field theoretical model of the human brain ", Frank TD & Daffertshofer A & Beek, PJ & Haken N, Physica D 1999; 127: 233-249

[26] "Coherence and stochastic resonance in a two-state system ", Lindner B & Schimansky-Geier L, Physical Review E June 2000; 61 (6): 6103-6110

Claims (54)

Method of electromagnetic modification of brain activity to achieve a given behavioral goal, using a Relationship between behavior and the course of brain activity characteristics descriptive behavioral model, as well as a brain activity model, what brain activity describes quantitatively, with brain activity characteristics and their courses can be derived from the quantitative description of brain activity are comprehensive - Determine by means of the behavior model, which target course of certain brain activity characteristics corresponds to the achievement of the behavioral goal, - generation exogenous electrical and / or magnetic fields, and measurement electromagnetic brain activity along with calculation of brain activity characteristics to achieve the target course in a control or regulating circuit, which includes calculations with the brain model. The method of claim 1, wherein a generic brain activity model is used by determining non-observables of the generic Brain activity model for one specific users to a specific brain activity model is calibrated, and the influence of exogenous electrical and / or magnetic fields determined on brain activity for this user becomes. A method according to claim 1 or 2, wherein as brain activity characteristics the brain activity descriptive electromagnetic quantities or from electromagnetic Sizes derived Auxiliary variables used become. The method of claim 3, wherein the electromagnetic quantities extra- and / or potentials and / or currents and / or measured intracranially Include magnetic fields. The method of claim 3 or 4, wherein the brain activity characteristics Frequencies of electromagnetic quantities and / or coefficients regarding a Fourier or Wavelet base development or Karhunen-Loève coefficients of time series of the electromagnetic quantities and / or stochastic Parameters such as e.g. stationarity Moments, correlations, and / or non-linear parameters, such as e.g. stability Ljapunov exponents, limit cycles, minimum phase space dimension. Method according to one of claims 1 to 5, wherein for calculating of the target course from the behavioral goal uses a behavioral model such as Variants of the Davidson model, the Ishihara-Yoshie model or the chaos seizure model according to [9]. Method according to one of claims 2 to 6, wherein the non-observables for one certain users can be calibrated. Method according to one of claims 1 to 7, wherein the influence an exogenous electric and / or magnetic field on the brain activity of a certain user is determined. A method according to any one of claims 2 to 8, wherein the computing the influence of the exogenous electrical and / or magnetic field and the determination of the non-observables can be carried out in a coupled manner. Method according to one of claims 2 to 9, wherein during calibration first a decomposition (S 2130) of actual courses of brain activity characteristics is made in HAC modes in such a way that these HAC modes are made up of the brain activity model calculated courses of brain activity characteristics correspond. The method of claim 10, wherein based on these modes sets of Parameters and endogenous inputs and / or exogenous inputs determined that are according to the brain activity model of the respective fashion. Method according to one of claims 1 to 11, wherein a test signal is calculated (S 2160), the test signal being an exogenous electrical and / or magnetic field that is applied to the user, being its brain activity is measured and the measured after or when the test signal is applied brain activity is evaluated together with previous brain activity and based on the evaluation result with the measured values compatible parameter sets together with endogenous inputs and influences of the test signal in the mode under consideration. The method of claim 12, wherein the test signal changes and is created again until both parameter set and endogenous input and the influence of the test signal on the mode under consideration is clearly established, whereby the fashion is calibrated. Method according to one of claims 12 and 13, wherein the calibration of brain activity through calibration of modes and interactions between modes done, the former and or both taking into account exogenous inputs. Method according to one of claims 1 to 14, wherein a quick calibration already after sending one or fewer test signals a set of parameters compatible with the measured values, endogenous inputs, exogenous Selects inputs. The method of claim 15, wherein based on the calibrated brain activity model the course of a control variable is calculated is, the actual course of the brain activity characteristics in the target course to transfer, being the control variable that of the exogenous magnetic and / or electrical Field corresponding exogenous input in the brain activity model represents. The method of claim 16, wherein the course to be calculated the control variables by using signals of known effect is simplified. Method according to one of claims 2 to 17, wherein for several Measuring ranges local modes are calibrated, their interactions through successive changes individual local modes can be determined (S 2420, S 2430, S 2440, S 2221). Method according to one of claims 1 to 18, wherein in addition to the electrical and / or magnetic fields sensory inputs and / or biochemically active substances are used. Method according to one of claims 1 to 19, wherein brain activity with the help a suitable repetition rate over the duration of the application changed beyond and or is stabilized. Method according to one of claims 1 to 20, wherein for measurement the electromagnetic brain activity interconnected several sensors become. Method according to one of claims 1 to 21, wherein for generating of the fields, a transmitter is used with regard to its position and orientation changeable is. A method according to any one of claims 1 to 22, wherein for producing of fields transmitters are used, with multiple transmitters switched together. Method according to one of claims 10 to 23, wherein decomposition an n-element time series in spatiotemporal modes becomes. A method according to any one of claims 10 to 24, wherein the decomposition performed with pursuit procedure becomes. A method according to any one of claims 10 to 25, wherein the decomposition after transformed output functions of the brain model, where the respective transformation involves a signal change between those at the brain activity represents neurons involved and the relevant sensor. A method according to any one of claims 10 to 26, wherein the decomposition after embedding in a metaphase space, which causes stationary and non-stationary parts become separable. The method of any one of claims 10 to 27, wherein the decomposition is carried out several times with different methods, the multiple Sets of Fashions depending be weighted by the procedures and only the modes for the further Methods are used whose total weighting is above one certain threshold. The method according to one of claims 1 to 28, wherein the behavioral goal comprises one or more of the following behavioral goals: change or non-change of perception and / or perceptive ability and / or willingness to perceive, change or non-change the ability to discriminate for certain stimuli or classes of stimuli, change or non-change of actions and / or ability to act and / or willingness to act, change or non-change of reaction speed, change or non-change of activation and / or activation ability, change or non-change of motivation and / or motivation ability and / or willingness to motivate, change or no change in attention and / or attentiveness, change or no change in memory and / or memory content and / or memory access, change or no change in learning and / or ability to learn and / or willingness to learn, change or no change in consciousness, change or No change in emotions and / or emotional ability and / or willingness to emotion, change or no change in appetites and / or aversions, change ng or no change in thinking and / or thinking ability and / or willingness to think, change or no change in behavioral processes, change or no change in behavioral correlations. The method of claim 30, wherein several are not necessarily compatible behavioral goals can be combined hierarchically. The method of claim 30, wherein the behavioral goal is seizure freedom the highest in the hierarchy priority is assigned. Method according to one of claims 1 to 31, wherein several weighted behavior models are used in parallel, with only those control or regulation steps (S 3000) are carried out that are compatible with a majority of these models. 33. The method according to any one of claims 1 to 32, wherein an external Validation of behavior change / maintenance for the performed by individual users becomes. 33. The method according to any one of claims 1 to 32, wherein several weighted brain activity models can be used in parallel, with only those control or Control steps (S 3000) are carried out with a plurality of these models are compatible. 35. The method of any of claims 2 to 34, wherein calibration requirements for individual sensors Calibration requirements for Amounts of sensors to be replaced. 36. The method of any one of claims 1 to 35, wherein the generating the fields and the measurement are carried out alternately. 36. The method of any one of claims 1 to 35, wherein the generating the fields and the measurement are carried out simultaneously. A method according to any one of claims 14 to 37, which is to test signal using linear or non-linear and / or chaotic and / or has stochastic and / or non-stationary components. 39. The method according to any one of claims 17 to 38, wherein the identification by users based on time-stable calibration data he follows. A method according to any one of claims 17 to 39, wherein calibration and modification steps for groups sensors and transmitters can be carried out sequentially or in parallel. A method according to any one of claims 17 to 40, wherein bifurcation points controlled, and thus among other things activated inactive modes become. A method according to any one of claims 18 to 41, wherein amplitude amplification is age-correlated activity flattening he follows. Method according to one of claims 1 to 42, wherein it is automatic accomplished becomes. Device for achieving a predetermined behavioral goal, using a behavioral model describing the relationship between behavior and the course of brain activity characteristics, and a brain activity model which describes brain activity quantitatively, brain activity characteristics and their courses being derivable from the quantitative description of brain activity, ins Special for carrying out a method according to one of claims 1 to 43, comprising: a device for determining by means of the behavior model which target course of certain brain activity characteristics corresponds to the achievement of the behavioral goal, a device for generating exogenous electrical and / or magnetic fields and application to a user with at least one transmitter, - a measuring device for measuring electromagnetic brain activity with at least one sensor, - a device for calculating brain activity characteristics to achieve the desired course in a control or regulating circuit. Device according to claim 44, characterized in that the measuring device has a plurality of sensors has, which form a sensor grid. Device according to claim 44 or 45, characterized in that that the device for generating exogenous electrical and / or magnetic fields has a plurality of transmitters which form a transmitter grating. Device according to one of claims 44 to 46, characterized in that that at least one computer unit is provided in the software units to implement the method according to one of claims 1 to 43 are stored. Device according to one of claims 44 to 47, characterized in that that for each sensor and each transmitter an electrical one and / or magnetic shielding is provided. Device according to one of claims 44 to 48, characterized in that that the measuring device can be mechanically decoupled from the rest of the device is designed so that the measuring device can be carried by a user can. Device according to one of claims 44 to 49, characterized in that that the sensors and transmitters are arranged extracranially. Device according to claim 50, characterized in that the Sensors and transmitters on the inside of a cranial shape of the respective user imitating helmet are arranged. Device according to one of claims 46 to 51, characterized in that that the transmitter grid and the sensor grid are so entangled that each transmitter is adjacent to sensors and each sensor transmitter are. Device according to one of claims 46 to 52, characterized in that that in the transmitter grid brackets for receiving additional Transmitters are provided so that the transmitter density of a transmitter grating locally changeable and / or so that the angle of inclination of individual transmitters to the User cranium changeable are. Device according to one of claims 44 to 53, characterized in that that the device has sensors and transmitters Head unit, an intermediate unit and a base unit is divided, the intermediate unit being a computer and software units for To run the non-calibration dependent Method steps and the base unit a computer and software units to run the calibration-dependent Process steps include.