WO2003085546A1 - Verfahren und vorrichtung zur elektromagnetischen modifikation von hirnaktivität - Google Patents
Verfahren und vorrichtung zur elektromagnetischen modifikation von hirnaktivität Download PDFInfo
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- WO2003085546A1 WO2003085546A1 PCT/EP2003/003545 EP0303545W WO03085546A1 WO 2003085546 A1 WO2003085546 A1 WO 2003085546A1 EP 0303545 W EP0303545 W EP 0303545W WO 03085546 A1 WO03085546 A1 WO 03085546A1
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Classifications
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- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/36014—External stimulators, e.g. with patch electrodes
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- A61B5/242—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
- A61B5/245—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
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- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
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- A61M2021/0055—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus with electric or electro-magnetic fields
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Definitions
- the invention relates to a method and a device for the electromagnetic modification of brain activity, in particular for the controlled or regulated electromagnetic modification of brain activity in vivo and the behavior modification resulting therefrom.
- Model-based controlled or regulated modification means targeted change or non-change of brain activity on the basis of a behavior goal, a behavior model and a brain activity model.
- Regulated means that observation, calculation and modification steps are linked together in feedback loops.
- the behavioral goal to be specified by the user includes the type and form of a certain behavior and its durability.
- Behavior refers to any spatiotemporal combination of any potentially validable factual or possible condition of a person.
- Behavior includes, but is not limited to, perception
- Behavior is based on brain activity.
- the relationship between the two is quantified using a behavior model, i.e. an empirically established link between certain behavior and a certain dynamic of certain brain activity characteristics.
- Brain activity characteristics are parameters of brain activity that can be determined from measured values. Examples of brain activity characteristics are potential differences between an EEG electrode and a reference electrode, or the band range of the most pronounced frequencies across all EEG electrodes ("alpha band” or the like [12]), or the similarity index ("similarity index”, see [5 ]) the measured values of MEG sensors.
- a brain activity model refers to a physiologically based model which shows brain activity by means of the dynamics of brain components and / or describes their interactions (this includes, for example, in [24], [1], [23], [25] or [18]).
- a non-observable is a parameter or a variable of a generic model that can only be measured in vivo with considerable effort - or not at all - with the necessary spatial and temporal resolution.
- Non-observables determine the brain dynamics, and fluctuate between individuals and within an individual up to a factor of one hundred.
- Generic brain activity models make no statements about direct external influences on the dynamics of brain components, at best about indirect external influences through receptor stimulation, deprivation or lesions.
- 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 with the aid of suitable methods.
- These brain activity models usually include nonlinear differential equations for example of the shape for each brain component
- x (t) f (x, t okale parameters, endogenous input, exogenous input)
- X is a characteristic of activity in the home, x its time derivative, t time, f a function, among other things, of the type of component ("local parameters"), the type and extent of the effects of other components spreading via physiological connections (“endogenous input”), and the direct external influence on the brain component (“exogenous input”).
- the endogenous input includes other (“translocal”) parameters, such as coupling strengths or delay times between components. Missing or inadequate specification of the brain activity model for the respective user leads to a wide spread in this case largely unknown effects of exogenous electromagnetic inputs and thus to the inapplicability of corresponding procedures outside of a range of neurological, psychiatric or psychological damage in which random effects are weighed against possible treatment success.
- 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 (e.g. by extracranial generated variable magnetic field, which effortlessly penetrates the user's cranium and induces intracranial induction voltages, which gives the possibility of influencing the brain activity of the user).
- In vivo relates to the use of the method in living users, in contrast to cell preparations, parts of the brain, computer simulations or the like, and thus places special requirements both in terms of the complexity of the brain processes taking place and in terms of the speed of observation, calculation and modification as well as security of the process.
- 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 approximately 10 15 connections between them), and has not been considered or attempted to date. The same applies to the associated behavior modification.
- NA nonlinear dynamic / neural network / artificial intelligence approach
- CON control theory approach
- MED medical approach
- NA A possible basic building block of NA is the neuroie oscillator, that is, an entity that can switch between oscillatory behavior and silence, and which can be composed of an ensemble of smaller entities (but which will not be considered further).
- NA deals with neural networks, i.e. small (order of magnitude ⁇ 10 4 ) connected ensembles of neural oscillators, and the phenomena that occur in these networks (eg memory or pattern recognition).
- neural networks i.e. small (order of magnitude ⁇ 10 4 ) connected ensembles of neural oscillators, and the phenomena that occur in these networks (eg memory or pattern recognition).
- Control procedures can be divided into model-based and data-based procedures.
- the quality of a model-based process is linked to the suitability of the model for the problem in question.
- no brain activity or behavior models have been used in humans or animals.
- the quality of a data-based method depends on the simplicity of the system to be checked (since the first step in a data-based method is usually the reconstruction of the phase space, in which search procedures are 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 come together: high to unachievable high demands on storage space and computer speed, potentially arbitrarily long waiting times until a target orbit is reached, - intermittent outbreaks the target orbit in systems with stochastic elements,
- MED is implicitly based on a microscopic physiological model with individual neurons as constituents of brain activity - which are to 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 dentrites etc. play an essential role in the calculation of 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-ural paradigm (with its 10 11 individual neurons and approx. 10 15 connections between them) is unsuitable.
- the MED approach based on this is affected by the apparent inexplicability or randomness of its clinical results (for transcranial magnetic stimulation -TMS - reference is made to the overview [3]).
- Other activity-modifying MED procedures are intra- and extracranial electroshocks. All MED procedures aim to depolarize neuron membranes and thus to generate more action potential (so-called "stimulation").
- stimulation the electromagnetic depolarization of individual neurons using the MED method can be described as "enslavement” (whereby sensory enslavement through rhythmic light flashes, acoustic signals, etc. should not be considered).
- Enslavement is the result of applying high-intensity artificial signals to a system that then takes on the externally specified signal pattern instead of its natural behavior during this application.
- NA it is known that with weak coupling, communication in neural networks depends on the commensurability of the frequencies with which the participating neurons oscillate.
- local enslavement with a frequency that is not commensurable or only weakly commensurate causes the enslaved neurons to be switched off from their normal communication, and thus generates a virtual lesion.
- Current MED procedures consider virtual lesions to be an essential effect of TMS - which is inexplicable in the one-natural paradigm.
- MED methods consider "stimulation" to be improved if a less high intensity is required to move individual neurons to fire, or if the spatial area covered by the enslavement can be more narrowly limited. Repeated enslavement on the same medically sensible locus can cause physiological changes that persist during the application period, which are positive in some cases (eg statistically significant improvement of certain types of clinical depression under repeated TMS, see eg [3]).
- current methods are individualized only in two ways: firstly, when a suitable locus of the individual neurons to be depolarized is found, secondly, when a reference value is found for the strength of the electromagnetic field leading to enslavement (given as a percentage of a "motor threshold").
- TMS is used diagnostically to determine 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 methods are preferably used for medical purposes, in which the disadvantages of enslavement (up to the unlikely case of seizures) are at least compensated for by their medical effects.
- Current MED procedures only include passive safety measures such as avoiding high-frequency enslavement, excluding patients with a tendency to epileptic seizures, switching off the electromagnetic field if a seizure should occur.
- the object of the present invention is to create a method and a device for the controlled or regulated electromagnetic modification of brain activity in vivo and the resulting behavioral modification.
- a brain activity model which describes the influence of exogenous electrical and / or magnetic fields on brain activity
- a behavior model is used which describes the relationship between brain activity and behavior. This makes it possible for the first time to specifically influence a person's behavior using exogenous inputs.
- the invention relates to the use of a brain activity model, as well as 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 the extracranial signal and the control force allow secure intervention within the framework of a control or regulating circuit, which in turn brings about a reliable achievement of a brain activity goal.
- the use of a behavior model ensures that the achievement of a brain activity goal results in the achievement of the individual behavior goal of the user.
- the invention is based on the knowledge that the relationship between behavior and the dynamics of brain activity characteristics is quantified using behavior models, and the dynamics of brain activity characteristics are quantified using suitable individualized brain activity models, stating suitable control parameters, so that a reliable intervention to achieve an individual behavior goal is possible is.
- FIG. 1 shows a transmitter in a sectional view
- FIG. 2 shows the transmitter from FIG. 1 in a view from below
- Fig. 3 is a planar projection of openings for sensors
- FIG. 4 schematically shows a helmet and carrier axis together with a chin rest of a head unit
- FIG. 5 shows a further planar projection of openings for sensors
- FIG. 7 HAC model modes with variation of ⁇ (extracts)
- FIG. 8 HAC model modes with variation of a (extracts)
- FIG. 18 HAC model modes for II under exogenous elementary loops of medium strength (examples),
- FIG. 21 flow chart of the S 2000 method (calibration)
- FIG. 23 flow diagram of the method S 2100 (local calibration, 2.
- FIG. 24 flow chart of the method S 2300 (translocal calibration).
- 26 is a diagram showing the data of an EEG channel
- FIG. 27 is a diagram showing a section of the data from FIG. 26 in the
- FIG. 29 is a diagram illustrating neuronal activity modeled in the phase space with the Wilson-Cowan model with the same input at 25 different mixing angles.
- the device 1 comprises 30 one or more head units 2 connected to an intermediate unit 3, one or more of which are connected to a base unit 4 (FIG. 6).
- a head unit 2 comprises a measuring system with devices for electromagnetic measurement data acquisition (“sensors”), measurement data preprocessing, and measurement data transfer to the intermediate unit, as well as a control system with devices 5 for generating electromagnetic fields (“transmitters”) and for implementing the the intermediate unit outgoing
- An intermediate unit comprises a computer with software, with which implement the methods for controlling brain activity when calibration is not necessary, as well as connections to and from the base unit.
- the base unit 4 comprises a computer which firstly contains a database with the model and user data, and secondly the other methods described in more detail below are implemented. The intermediate and the base unit are located on the same or 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 formed, for example, from a SQUID sensor element with a suitable evaluation device for detecting a magnetic field and a cooling device.
- the EEG sensors have e.g. B. two electrodes for measuring an electrical potential difference.
- An advantageous embodiment of a sensor comprises its partial or complete electrical and / or magnetic shielding from its surroundings, insofar as this does not hinder its function.
- An advantageous embodiment of the parts of the measuring system close to the head comprises a multiplicity of sensors which are distributed intracranially and / or extracranially; this multiplicity of sensors is referred to as a sensor grid.
- An advantageous embodiment of an extra-cranial sensor grid includes fixing it to the crane of the respective user, so that when the sensor grid is put on and taken off several times, the sensors return to their respective relative positions, for example by fitting the sensor grid into a helmet, the inside of which has the cranial shape of the respective user replicates.
- Another advantageous embodiment of the sensor grid comprises implanted electrodes.
- An advantageous embodiment of an extracranial transmitter 5 comprises a current-carrying coil 6 with a para-, dia- or ferromagnetic core 7, as shown schematically in a sectional view in FIG. 1, the arrow directions symbolizing the directions of the current flow.
- the transmitter 5 essentially has a cylindrical shape, the outer surface and an end face of the cylinder forming the rear side being clad with a shield 8.
- the coil 6 and the core 7 directly adjoin the side of the transmitter which is free of the shielding, and with this side the transmitter 5 is aligned with the cranium during operation to emit exogenous magnetic fields.
- a holding element 9 is arranged, with which the transmitter 5 can be fixed in a helmet.
- the extracranial transmitter 5 must be protected against deformation, for example by pouring the live parts in suitable resin, or embedding the live parts in stable insulating material.
- the transmitter 5 can be provided with a cooling device.
- Another advantageous embodiment of an intracranial transmitter comprises implanted electrodes.
- An advantageous embodiment of the parts of the actuating system close to the head comprises a plurality of transmitters which are distributed intra- and / or extracranially; this arrangement of transmitters is referred to as a transmitter grid.
- An advantageous embodiment of an extracranial transmitter grating comprises fixing it with respect to the crane of the respective user, so that the transmitter grids take up their respective relative positions again when the transmitter grille is put on and taken off multiple times, for example by fitting the transmitter grille into a helmet, the inside of which is the cranial shape of the respective user replicates.
- the sensor and / or transmitter grating can also be fixed with the aid of a camera, the position of the user's head in the room, as well as the sensors and / or transmitters with respect to the head, being recorded using a plurality of cameras and converted in real time into 3D data .
- intracranially implanted electrodes are used as sensors and / or transmitters, via which EEG measurements can be carried out and currents can be conducted into the brain. Lines leading to these electrodes and / or their interfaces to the computer unit and / or further lines and / or further measuring devices and / or the associated computer unit and / or the energy supplier of electrodes and / or computer unit can also be implanted, thereby permitting outpatient operation.
- FIG. 3 shows a planar projection of the superimposition of the transmitter with the sensor grid (openings 13, sensors as circles and openings 14 for transmitter 5 are shown as quadrilaterals).
- the user sits on an armchair with a neck support below the helmet 10.
- the sensor grid is intracranial and the helmet contains the extracranial transmitter grid.
- the transmitter grid is intracranial and the helmet contains the extracranial sensor grid.
- both sensor and transmitter gratings are intracranial.
- the sensor density or sensor configuration of an extracranial sensor grid can be set. In a further advantageous embodiment, this change is automated, controlled or regulated via the intermediate unit.
- the transmitter density or transmitter configuration of an extracranial transmitter grid can be set and / or the angle of inclination of each individual transmitter to the user's cranium can be changed.
- a planar projection of a mechanical holder of this embodiment is shown in FIG. 5.
- openings for sensors 13 are shown as circles and openings 14 for transmitters.
- all conventional coil configurations can be represented with their arrangement, orientation and field direction.
- the device is provided with conventional protection against power failures and / or voltage fluctuations.
- the intermediate unit can run real-time and automatic procedures for density and positioning optimization of sensors and transmitters, as well as for the elimination of artifacts, i.e. non-random measurement value distortions caused by artificially generated magnetic fields, eye movements, muscle twitches, etc. arise.
- 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 are marked with "D”. The individual steps are explained below:
- the behavior model represents the connection between
- HAC stands for both singular and plural for "home activity characteristic”.
- the HAC are calculated from measurement signals according to certain regulations (D 2200). Examples of HAC are potential differences measured by a sensor (electrode plus reference electrode), or a power spectrum of the signal received by the sensor within a time window, or the quotient of beta (13-30 Hz) - EEG activity to alpha (8-12 Hz) -EEG activity for this sensor
- Examples of a behavioral model are the reduced Davidson model (see e.g. [11]), according to which positive emotions with a higher quotient from beta (13-30 Hz) - EEG activity to alpha
- the behavioral model (D 3000) allows the behavioral goal (D 2000), which basically includes the durability of the desired behavior, to be converted into a target course of the HAC (D
- HACs The type and number of HACs result in minimum equipment requirements (D 4000). For example, one uses to calculate the quotient of beta (13-30 Hz) - EEG activity
- Alpha (8-12 Hz) - EEG activity in the left-frontal cortex at least one EEG electrode plus one reference electrode.
- Alpha (8-12 Hz) - EEG activity in the left-frontal cortex at least one EEG electrode plus one reference electrode.
- For the controlled achievement of the target course of the above HAC uses at least one transmitter.
- the HAC used are to be calculated from the variables of the generic brain activity model used (D 1000).
- Wilson-Cowan model (see e.g. [1]) can be calculated.
- the HAC results in minimum calibration requirements (D 1100) with regard to the generic model used. The less robust the calculation of the HAC, the stronger
- 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, as well as 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 used in the following
- the calibration results in a specific brain model (D 1200), which quantifies exogenous input as a control variable within the model (D 1300). Calibration is detailed in FIG. 21.
- the first goal of the local calibration is to limit the brain activity in the detection range of each sensor for a specific user in a specific time interval
- 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 (process shown in FIG. 23).
- FIG. 21 shows a section of the method in which the decision according to decision step E 100 described above is made with the data D 1100, D1110 and D 1120 already explained above a calibration should be carried out or not. If no calibration is to be carried out, the method goes to step S 3000, as already explained above. If, on the other hand, a calibration is to be carried out, the method proceeds to step S 2100, in which a local calibration is carried out, which is discussed in more detail below with reference to FIGS. 22 to 23.
- step S 2300 Since brain activities are locally calibrated during local calibration, the same brain activity may be recorded and calibrated several times by different sensors or sensor units. It is therefore expedient to identify identical brain activities as such in step S 2300, multiple-calibrated brain activities being taken into account only once or multiple calibrations of one brain activity being evaluated in combination. The corresponding data are then integrated into the data records D 1200 and D 1300.
- step E 120 If a translocal calibration to D 1110 should be necessary, this is checked in step E 120 and, if necessary, a translocal calibration S 2400 is carried out, which is explained in more detail below with reference to FIG. 24.
- the corresponding data is then inserted into data records D 1200 and 1300.
- brain activity is measured with each sensor EM (S 2110).
- the resulting time series (D 2205) is converted into an actual course of HAC (S 2120), which results in actual courses of HAC (D 2210).
- a brain activity model (D 1000) is usually described using differential equations, into which exogenous input is integrated under certain assumptions (D 1049).
- a model mode is a solution of the differential equations of the brain activity model expanded in this way for a set of non-observables, ie of parameters, endogenous and exogenous inputs. They are calculated in step S 2115 and stored in a database (D 1051). This database contains the assignment of sets of non-observable model modes. The HAC curves of the model modes are calculated using the data stored in the database D 1051 and / or data calculated directly in step S 2115 (S 2120). This results in theoretically possible HAC courses (D 1030).
- HAC model modes result in similar (i.e. 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 analogously in gauge 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 different potentials, which in turn can be the basis of the electric / magnetic fields.
- electrodynamics corresponding to a HAC here, for example, "energy density in a space sector”
- stochastic modes for example, their p-moments are stored over time.
- Model modes and possibly HAC modes are stored in a database and There are only a finite number of sets of non-observables stored (grids), the same for fashion modes and HAC model modes. If necessary, there is the option to refine and / or expand the grid (e.g. with new parameter sets and / or new species and / or new coefficients endogenous and / or exo inputs).
- 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.
- HAC model modes that may have changed according to a priori assumptions (D 1040, FIG. 22) are identified in the above-mentioned actual course of HAC (S 2130).
- the image of a HAC model mode is proportional to the HAC model mode itself and (except in cases of negligible amplitude) can 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.
- a (HAC) model mode is local and thus describes localized brain activity. Decomposition according to S 2130 (see FIG.
- HAC model mode D 1030
- 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 result of the decomposition is a list of relevant HAC model modes (D 2300) in the measuring range of the respective sensor, a list of possible model modes for each element of D 2300 (D 2350) and a list of possible parameter sets, as well as possible endogenous inputs for each model mode in D 2350 (D 2400).
- test signal results from the possible endogenous and exogenous inputs (comprising: constant, oscillating, with elementary loops, i.e. time-delayed use of a measurement signal as a transmitter signal, and many others).
- test signal is integrated into the generic brain model as follows: replace "input” in the model equations with "endogenous plus exogenous input”.
- the translation operator Ü represents the relationship between the exogenous, physically measurable signal and control variables in the equations of the specific, i.e. H. calibrated, brain model.
- the specific, i.e. H. calibrated, brain model i.e. H. calibrated, brain model.
- different values of the control variable change the relevant mode, which in turn is physically measurable.
- test signal has the task of separating between different sets of parameters, endogenous inputs, and possibly 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 an usually time-dependent shape at the location of the transmitter, and a - possibly one-element - sequence of amplitude multipliers.
- the extracranial magnetic field can be generated with 0.01 Tesla with the aid of a coil through which a current of strength lo flows.
- 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) (the voltage is assumed as a variable of the brain activity model, t is the time in Milliseconds)
- the duration of the partial signals 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 influencing invariants and assumptions (D 1050).
- the possible amplitude sequences are subject to technical and / or health conditions (D 4100, D 4200).
- test signal This numerical calculation usually results in a lot of possible shapes for test signals, each with a sequence of possible amplitude multipliers.
- An optimal test signal is then selected from this set using predefined criteria (for example, minimal disturbance of all brain activities that occur with the exception of the mode to be examined by the test signal).
- One begins with the transmission of the signal (S 2170) with the first amplitude of the sequence, measures at least with the considered sensor S ⁇ and at most with all sensors of the sensor grid (see eg FIG. 5) the electromagnetic brain activity (S 2110), time series are obtained therefrom (D 2520), from which curves of HAC are calculated (S 2120).
- 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 FIG. 22) are permitted for this, where instead of the "exogenous input equal to zero" there is now a possibly other exogenous input, the shape of which results from the invariants and assumptions of influence, and its Strength usually results from the (usually non-linear) changes at different amplitudes of an amplitude sequence.
- step S 2300 following local calibration in step S 2300, it is possible to identify identical carriers of model modes.
- a mn (t) as a mode assigned to the sensor S m , characterized by parameters P mn , endogenous input l mn (t) and translation operators Ü mn ⁇ ( ⁇ ) ( ⁇
- Modes do not have the same carrier if there is a transmitter T, so that Ü kl ⁇ (X) is not equal to Ü kn ⁇ ( ⁇ ) and / or Ü mt ⁇ ( ⁇ ) is not equal to Ü mn ⁇ ( ⁇ ).
- Method step S 2300 is not essential, but causes a possible downsizing of the functional matrix (D 2410, see FIG. 24) in the translocal calibration described below.
- the translocal calibration determines 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, conditional delays, and is modeled as part of the endogenous input).
- the tool for this is the "functional matrix" (D2410): if a total of n different model modes have been found in the measuring range of at least one of the sensors, then this functional matrix is an n * n matrix, in the (ij) th cell of which an entry can be found if the mode i has a demonstrable influence on the mode j.
- the entry quantifies this influence, so that in the equations of the brain model for the mode j "endogenous input" is replaced by "possibly delayed function of the i-th mode plus other endogenous input”.
- the method for filling required parts of the functional matrix is shown in FIG. 24.
- Knowledge of the functional matrix or parts of the functional matrix allows the calculation of several constituents of the phenomena comprising brain activity model (e.g. synchronization, phase locking, and much more) using conventional numerical methods, neuro networks, nonlinear dynamics, and the like.
- the translation operators (D 1300) obtained from the local calibration as well as other non-observables allow a HAC forecast (S 2410) for all modes, which takes translocal influences into account only in summary. If a mode is actively changed (S 2420), a possible difference between the actual HAC and the forecast HAC can be determined. This is recorded in step S 2430. This
- step S 2440 those functional matrices are calculated whose inclusion in the equations of the brain activity model are suitable for explaining the detected difference.
- test signal is modified in step S 2221 to further narrow the amount of possible functional matrices. This iteration is terminated as soon as at least the desired elements of the functional matrix have been clearly identified.
- the aim of the EM control or regulation (S 3000) of brain activity via feedback based on brain activity model is to achieve and maintain target courses of HAC. This is shown in detail in FIG. 25.
- transmitter signals are calculated that are theoretically suitable for converting the actual course of the HAC (D 1010) into the target course (S 3100).
- 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 brain activities) and spatial composition (signals from several transmitters). , as well as temporal composition (sequence of signals).
- the best transmitter signal is selected and sent using a utility function (e.g. minimum field strength per transmitter) (S 3400).
- the Davidson model is based on the power spectrum of EEG signals.
- a suitable HAC is therefore a sequence of squared absolute amounts of Fourier coefficients, which is calculated via Fast Fourier Transformation, for example at the frequencies from 1 to 50 Hertz. Derived HAC is
- HAC (100) is the performance spectrum with regard to the measured values 1 to 100, HAC (101) the performance spectrum with respect to the measurement 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: Pos (t, with influence)> 2 * Pos (t, without influence). This requirement determines all target courses of the HAC.
- D 4000 minimum equipment requirements:
- a neural oscillator does not oscillate or oscillate, depending on its input, as well as non-observable physiological parameters.
- (transmitter signal) denotes the exogenous input into the neural oscillator under consideration on the basis of 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, and on the type of signal sent. Translation operators are not observable.
- S 800 (specification of the minimum calibration requirements): ⁇ , a, b, c, d, p x , p y , translation operators for a sensor-transmitter pair.
- D 4100 Equipment restrictions: Depends on the equipment used, e.g. conventional EEG adhesive electrodes on the input side, 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 to one transmitter means that different modes cannot be influenced independently of one another.
- D 1200 (specific brain activity model):
- D 1200 (specific brain activity model):
- the infinite number of possible parameter combinations in the generic brain model are reduced a priori to a computationally manageable number: firstly by calculating the limits of the parameter range in which endogenous inputs exist that lead to a non-constant EM output, secondly by discretizing an n-fold this range (defining 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.
- the starting values for the calculation of the activity variables x and y in the simplified Wilson-Cowan equations are randomly distributed in the interval [0,1].
- Figure 8 shows the HAC of modes varying the
- Figure 10 shows the HAC of modes varying the
- FIG. 11 shows the HAC of modes with variation of the self-locking parameter d:
- HAC HAC measuring invariants and assumptions: In order to be able to recognize model modes in empirical modes, HAC are selected in the simplest case, which are wholly or partly invariant to the selected measurement method, or for which this invariance can be assumed. In the present example (range of services), this assumption is: the attenuation between the wearer of a model mode and the sensor is frequency-independent. This means that the ratio of the coefficients of a model mode is retained on the way to the sensor. As a further assumption, the signal is considered piecewise stationary (with stationarity on average significantly longer than the length of the Fourier window).
- the EM variables that are described in the brain activity model are preset as model-related HAC.
- 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.
- a standard e.g. L-i
- the actual power spectrum modulo noise stationary has maxima at 10 Hertz with a magnitude of 10, at 20 Hertz with a magnitude of 5, and smaller maxima with further multiples of 10 Hz
- the actual power spectrum is approximately equally good, the first (E- ⁇ , - ⁇ ) with a weakening of approx. 10 / 2.2, the second (E ⁇ , 2 ) with a weakening of approx. 10 / 6.8.
- Removing, for example, the first HAC model mode * 0.22 provides approximately the zero spectrum.
- D 2350 (list of possible model modes for each element of D 2300):
- This list contains, among others, the model modes Mi.ii (for E- ⁇ , ⁇ ) and M ⁇ , 2 , ⁇ (for E- ⁇ , 2 ), the generation of which is dealt with below.
- D 2400 (list of possible parameter sets with endogenous input for each model mode in D 2350):
- the control variables in the equations of the generic brain model result from the application of the (as yet unknown) translation operator Ü belonging to a model mode and to the transmitter Ti used on a transmitter signal.
- ⁇ x -x + S (ax -by + p x endo + Ü x (transmitter signal))
- S 2160, D 2500 Any signal that can be implemented within the limits of the equipment (D 4100) and health limits (D 4200) can be used as a test signal.
- test signal is firstly to eliminate from D 2400 candidates for generating parameters and endogenous inputs of the model modes of the observed HAC model modes until their assignment to the respective observed HAC model mode is clear, and secondly to determine the translation of the test signal into exogenous input .
- Test signals that meet these conditions can be determined numerically from the set of all possible test signals (for example, selection 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.
- a distinction is made between frequencies incommensurable with the oscillation frequency and commensurable frequencies.
- 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): According to FIG. 12, the fundamental frequency of the two similar HAC model modes is 10 Hz. Sinusoidal signals of the frequencies thus come as incommensurate test signals 3 Hz, 7 Hz, 9 Hz, 11 Hz etc. in question.
- test signals sinusoidal signals of the frequencies 2 Hz, 4 Hz, 5 Hz, 6 Hz, 8 Hz, 10 Hz etc. can be used.
- phase shifts are irrelevant for the performance spectrum of a mode on a carrier.
- exo (t) x (t-10) -y (t-10).
- Test signal (0-05 Tesla, sin (2 * Pi * t * 3/1000), (1, 2,3,4)), t time in milliseconds.
- S 2170 (transmission of the test signal): takes place in the order specified in D 2500, with pauses between two transmission processes in the order of multiples of the membrane time constant, to avoid temporal summation effects.
- Multiplying the signal intensity allows the influence calibration. If, for example in the case of Mode II, doubling the signal intensity to l 2 causes the ratio of the first peak to the second peak to drop from 6 to 2.5, then the comparison with the HAC model mode database reveals, for example, that h corresponds to a multiplier of 2 (FIG. 14 (i) and l 2 a multiplier of 4 (Fig. 14 (ii)).
- the previous example avoided the complication that frequently occurs in reality that measurement and transmission cannot take place simultaneously. If the measurement interruptions are small compared to the oscillation period, the uninterrupted measurement signal is usually reconstructed using standard signal theory methods. In the following, such an uninterrupted measurement signal is always considered as given. Transmission interruptions are exemplified in the case of elementary loops from exogenous input.
- the switchover time is selected, for example, as the delay time. In this example, you switch back and forth between measuring and sending every 10 milliseconds. Then in the above equations:
- xi (t): if (t mod20> 10, x (t-10) -y (t-10), 0) in generic code
- test signal separates between modes I and II, for example via frequency shift (FIG. 15 (ii) compared to FIG. 16 (ii)) and / or
- Tuning calibration additionally sequences are out of phase
- D 4300 Cannot be realized with just one transmitter. Will be explained in example 3.
- 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 arranged according to predefined utility functions (for example, the lowest possible frequency, the smallest possible field strength, etc.). The best possible transmitter signal is then automatically selected.
- 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. Only one model mode is relevant in the present example, the HAC prognosis is therefore obtained by dividing the HAC shown in FIG. 13 (i) by the attenuation factor 10 / 2.2.
- the following applies: subtraction of the actual HAC curve from S 2120 and the forecast HAC curve (D 4040), amount formation for each frequency between 1 and 50 Hertz (deviation maximum of these amounts), and secondly, the achievement of the behavioral goal (of the target HAC curve) is determined analogously by comparing the actual and target HAC curves.
- the brain activity goal is reliably achieved by calibration and subsequent controlled modification, and thus also the behavior goal when the reduced Davidson model applies.
- D 4000 surface EEG with one electrode Si left front with reference electrode on the left ear, one electrode S 2 right front with reference electrode on the right ear (see [16]), two extracranial coils (transmitters Ti and T 2 ), where, for example, Ti is located on a connection line running on the head surface between Si and a central electrode Cz (see [16]) in the immediate vicinity of S, and T 2 analogously between S 2 and Cz, the distance between Si and Ti being the same is the distance between S 2 and T 2 . Everything else is as in example 1.
- Simplified Wilson-Cowan model as in Example 1.
- S 800 (specification of the minimum calibration requirements): ⁇ , a, b, c, d, p x , p y , translation operators for the four sensor transmitters
- D 1200 (specific brain activity model):
- D 1100 (minimum calibration requirements): As before.
- D 1110 (calibration type):
- D 1120 (scope of calibration): sensors Si, S 2 , transmitter Ti, T 2 .
- D 1200 (specific brain activity model):
- the combined signal of T- ⁇ , xi ⁇ (t) with 0.4 Tesla and T 2 , xi 2 (t) with 0.4 Tesla in Mode I leads to an increase in Pos to over 200% of the original value (see Example 1), in Mode II to a reduction of Neg to below 50% of the original value (compare Figure 12 (ii) with Figure 18 (iv)).
- the brain activity goal is thus achieved, as is the behavior goal if the Davidson model applies.
- Example 3 should not be explained in the same detail as the previous examples (e.g. all modes, local calibration of all sensor-transmitter pairs, etc.), but mainly with regard to significant deviations from the previous examples:
- Fm ⁇ (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 below 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
- 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 extracranially in the immediate vicinity of the relevant electrode: Ti anterior with respect to Fz, T 2 posterior with respect to Cz. Everything else as in example 1.
- Att ⁇ (with influence) Att ⁇ (without influence, with full driving)
- Att 2 (with influence) ⁇ Att 2 (without influence).
- “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 ".
- ⁇ tt: ⁇ f * / ⁇ f > .
- h represents the strength of the influence of the lth on the jth neural oscillator
- ⁇ in question represents the delay in the effect of this influence
- D 1110 (calibration type): translocal (includes local).
- Local calibration here expediently includes phase shift of test signals.
- the HAC minimum of the driven mode supplies the phase shift 3 required to switch off the mode.
- an interrupted sinusoidal signal as shown above, which is emitted by the transmitter Ti with a delay of 3 and with 0.5 Tesla, will largely switch off Mode III. (Result of this see Figure 19 i).
- the only S 2 mode is, for example, the Sr mode from Example 1. Neither of the above Tr signals should have any influence on the S 2 mode, nor should 0.1 Tesla sine signals from T 2 on the theta mode.
- these translation operators are, for example, zero for the two signals, as shown in FIG. 22.
- the result includes a functional matrix, which has the following form, for example:
- the Fm ⁇ is, for example, when driving has dropped to zero (see FIG. 19 i) using the interrupted sinusoidal signal by Ti close to its natural power spectra (“full driving”, see FIG. 19, (ii) and (iii)) stabilized without interfering with the brain activity detected by S 2.
- the result (measured from Si) is shown in Figure 19 (iv).
- the brain activity goal of restoring Att-i without interfering with Att 2 has been achieved with the applicability of the Ishihara-Yoshii model thus also the behavioral goal.
- 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
- the function f depends on the brain activity model used, and determines the derivative of x using x, t, as well as non-observable endogenous and non-observable exogenous input for a non-observable parameter set.
- Brain activity and behavioral models can also be represented in an integrated form.
- test signal suitable variation of a transmitter signal with a field strength that is as small as possible
- the dynamic calibration presented here differs both from existing mathematical methods for parameter estimation (which usually assume distribution assumptions of the parameters considered as random variables, and by observing the course of trajectories assuming an increasing approximation of the parameters calculated from the observations and the true parameters infer the values of the latter, see, for example, [7]), as well as adaptive controls / parameter estimates known from engineering (these are essentially only useful for linear time-invariant systems, in general, as a component of the control, keying is performed with very simple functions as manipulated variables a minimization of "ideal control variable minus actual control variable", see for example [8]).
- dynamic calibration is based on "active measurement” (transmission of signals into the relevant system and measurement of the system in interaction with the signal).
- active measurement transmission of signals into the relevant system and measurement of the system in interaction with the signal.
- the result of active measurement is often significantly different in non-linear deterministic and / or non-linear stochastic systems (such as the human brain) from the impulse or jump responses or the like (eg stochastic resonance) that are common in the measurement of linear systems.
- dynamic calibration is first of all a control separating and this preceding process, secondly, in dynamic calibration, the main focus is on the test functions used, the variety, and thus separability, between different sets of non-observables is appropriate to the complexity of the system to be examined (in contrast to simple test functions such as Heaviside functions or the like , whose system response often has to be observed over long periods of time, which is also not reasonable in the in-vivo case), thirdly, the type of approximation to a parameter set that explains the measured values is fundamentally different (successive reduction of subsets of a discretized parameter diversity, in contrast to the usual target -Is- comparison of controller sizes).
- dynamic calibration provides the unknown translation operators of parameters external to the system (e.g. field strength and / or frequency pattern of extracranial magnetic fields), which are crucial in the case of heterogeneous and / or complex systems, in exogenous input of the system equations.
- a upstream B means "change from A changes B", equivalent to: B downstream A.)
- the preferred embodiment of the method with local modification comprises the following elements (assuming that a cluster of neighboring Wilson-Cowan oscillators can be modeled by a pair of Wilson-Cowan equations): a) data analysis with Fourier analysis, alternatively also in combination with Wavelet analysis (suitable linear combinations of solutions x (t) and y (t) of the Wilson-Cowan equations as a basis) (basics see [13]), phase space embedding, as well as statistical methods to determine the probability of agreement of the above analysis methods Determine components of the measured signal with an equivalence class of solutions of the Wilson-Cowan equations.
- Test signals preferably linearly combined from time-delayed feedback of measurement signals and / or solutions of the Wilson-Cowan equations, ie for example ⁇ • (x (t - ⁇ ) - y (t - ⁇ )).
- b) Local calibration ie for example ⁇ • (x (t - ⁇ ) - y (t - ⁇ )).
- c) Modification as described d) Continuous seizure warning along with automatic counter-control / regulation as for example in [9].
- phase space embedding is illustrated for a measuring channel:
- FIG. 26 shows the EEG data of a channel, the x-axis representing time units (1/128 of a second), the y-axis voltage differences (between the measuring and the reference electrode at the respective time).
- the orbit (s) are determined, for example, according to [29].
- the x axis for a window with a length of t 32 is ⁇ -x (t-86) + ß-y (t-86), and the y-axis ⁇ -x (t) + ß-y ( t).
- alpha is set to 1 and beta to -1. Since multiple assignments of the same point are not shown, the number of points in FIG. 27 is less than 32.
- FIG. 28 shows the same axis designations as FIG.
- different time constants T are used for x and y in the Wilson-Cowan equations and / or different multipliers. They are calibrated as already described for the other parameters.
- the shape of the sigmoid function of the neurons of a cluster is calibrated with the aid of noisy test functions, since the cluster amplifies or suppresses signal or noise differently depending on the sigmoid function (see for simple networks, for example. [28] ).
- test functions are used one after the other with reversed polarity, that is to say first using a transmitter signal xi (t), then using -xi (t).
- the calibration is terminated after n> 0 test signals (“quick calibration”), a set of parameters that is compatible with the measured values and endogenous and exogenous inputs are provisionally determined as the calibration result. Additional measured values that result from the modification (S3000) become ongoing used to adapt the above sentence.
- influence assumptions and invariants are validated.
- the EM method is combined with sensory input (acoustic, optical, etc.).
- 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.
- suitable repetition rates is known, for example, for applications of TMS (transcranial magnetic stimulation with magnetic fields of 1-2 Tesla) (see, for example, [3]).
- sensor optimization is operated together with the calibration: switching on previously inactive sensors and / or changing the position of sensors and / or changing the orientation of sensors in such a way that these identified and / or unidentified brain activities are detected particularly well by the relevant, possibly reoriented sensor .
- Conventional optimization methods are suitable for sensor optimization.
- 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: Si, S 2 , S 3 , S, via a function f (S ⁇ , S 2 , S 3 , S 4 ) 0.3 * S ⁇ + 0.5 * S 2 +0.02 * (S 3 ) 2 +0.5 * sin ( S 1 + S 4 ) to S V i rt uei ⁇ connected together.
- a suitable f is determined for each subset of sensors using conventional optimization methods.
- transmitter optimization is operated together with the calibration: switching on previously inactive transmitters and / or changing the position of transmitters and / or
- transmitter optimization is operated by interconnecting existing transmitters to form virtual transmitters, such that brain activities are particularly well influenced by the virtual transmitter in question.
- the optimum results from the translation operators using conventional optimization methods
- the decomposition for n sensors (n integer between 1 and the maximum number of sensors used) is carried out simultaneously, i.e. the time series to be broken down is an n-tuple of one-sensor time series.
- the decomposition for an n-tuple of one-sensor time series is carried out using the pursuit method (e.g. matching pursuit, see [13]).
- the spatiotemporal decomposition of the n-tuple of one-sensor time series is carried out, for example, using the Karhunen-Loeve method (see, for example, [17], [18]) and / or independent component analysis (“ICA”) , see for example [22]).
- Karhunen-Loeve method see, for example, [17], [18]
- ICA independent component analysis
- the decomposition takes place after embedding in a metaphase space, as a result of which stationary and non-stationary parts are separated (see, for example, [15]).
- the decomposition is carried out in parallel according to several methods, only the modes (with an a priori defined weighting) identified by a plurality of these methods for further processing (including relevance checking) being accepted.
- the change or non-change of perception and / or ability to perceive and / or willingness to perceive is set as a behavioral goal. An example of this is the change in the ability to discriminate for certain stimuli or classes of stimuli.
- the change or non-change of actions and / or ability to act and / or willingness to act is set as a behavioral goal.
- An example of this is the improvement of reaction speed.
- the change or non-change of activation and / or activation ability is set as a behavioral goal.
- the change or non-change in motivation and / or motivation ability and / or motivation readiness is set as a behavioral goal.
- the change or non-change in attention and / or attention ability is set as a behavioral goal.
- the change or non-change of memory and / or memory content and / or memory access is set as a behavioral goal.
- the change or non-change of learning and / or ability to learn and / or willingness to learn is set as a behavioral goal.
- the change or non-change of consciousness is set as a behavioral goal.
- the change or non-change of emotions and / or emotional ability and / or emotional readiness is set as a behavioral goal.
- the change or non-change of appetites and / or aversions is set as a behavioral goal.
- the change or non-change of thinking and / or ability to think and / or willingness to think is set as a behavioral goal.
- the change or non-change of behavioral processes is set as the behavioral goal.
- the change or non-change of behavior correlations is set as the behavior goal.
- D 2000 several behavior goals that are not necessarily compatible are combined hierarchically, i.e. a priority list of the behavioral goals is drawn up and, in the case of procedural steps which may conflict with one another, the procedural steps belonging to the lower-priority behavioral goal are carried out later or not at all.
- D 2000 that of freedom from seizures with top priority is added to other behavioral goals, which includes an application of [9] to non-medically stressed users.
- the required behavior model is taken from the extremely extensive literature (e.g. for the presented examples from [11], [20], [21] dozens of publications with the same / similar subject).
- D 3000 several behavior models are used in parallel, only those control or regulation steps (S 3000) being carried out which are compatible (with a weighting defined a priori) with a plurality of these models
- external validation of the change / maintenance of behavior for the individual user during and / or after the EM application is generally carried out with the aid of psychological tests. In this way, it is checked to what extent the used, generally statistically validated behavior model applies to the user concerned.
- D 1000 several generic brain activity models are used in parallel, only those control or regulation steps (S 3000) that are compatible (with an a priori defined weighting) being compatible with a plurality of these models.
- individual sensors and / or individual transmitters are replaced by sets of sensors or sets of transmitters in the respective steps. This is used for the determination and controlled modification of nonlocal spatiotemporal modes, such as those that occur with Jirsa hook models (see, for example, [18]).
- D 4000 the minimum equipment requirements are exceeded in order to be able to obtain additional information that can be useful in later applications with possibly changed behavioral goals (e.g. basic application of the 10/20 surface EEG, see e.g. [16]).
- the advantage of this configuration lies in the possible standardization of parts of the apparatus and the possible use of commercially available hardware.
- the minimum equipment requirements are exceeded in order to have additional control or regulation options (for example: for each non-reference electrode of the 10/20 surface EEG, a coil located directly in front of this electrode).
- additional control or regulation options for example: for each non-reference electrode of the 10/20 surface EEG, a coil located directly in front of this electrode.
- the exogenous EM fields used as the control variable are supplemented by sensory inputs and / or biochemically active substances, with nothing fundamentally changing in the process steps shown.
- signals with stochastic components are used as test and transmitter signals (in particular in the case of brain activity models used with explicit stochastic components, such as, for example, [25], an extension of [18], with the aim of utilizing stochastic resonance and Coherence Resonance, see for example [26] for the FitzHugh-Nagumo model).
- signals with chaotic components are used as test and transmitter signals (in particular with used brain activity models with explicit chaotic components, such as extensions of [1] with locally strongly coupled oscillators).
- inactive modes are activated directly by transmitter signals, i.e. through the direct effect of the signals on fashion.
- modes are indirectly influenced by transmitter signals, i.e. via direct influence on modes, which in turn (see functional matrix) has effects on the modes to be influenced indirectly.
- bifurcation points are checked, i.e. qualitative changes are made to EM brain activity (as in example 3, where the neuro oscillator that was inactive without driving was switched on).
- inactive modes are activated.
- the amplification of amplitudes takes place by activating inactive modes and / or synchronizing modes, analogously monitoring by switching off active modes and / or desynchronizing modes.
- age-correlated flattening for EEG, see for example [12] counteracting amplitude amplification.
- the decomposition of non-stationary signals is initially carried out into a stationary and a non-stationary part (see, for example, [15]), the test and transmitter signals used then being modulated with the non-stationary part.
- users are identified on the basis of time-stable calibration data.
- EEG data have time-stable components for an individual (proven for up to five years, for example [10]).
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AU2003221553A AU2003221553A1 (en) | 2002-04-05 | 2003-04-04 | Method and device for the electromagnetic modification of cerebral activity |
JP2003582663A JP2005528937A (ja) | 2002-04-05 | 2003-04-04 | 脳活動の電磁修正のための方法と装置 |
EP03717275A EP1493097A1 (de) | 2002-04-05 | 2003-04-04 | Verfahren und vorrichtung zur elektromagnetischen modifikation von hirnaktivität |
US10/959,033 US20050124848A1 (en) | 2002-04-05 | 2004-10-05 | Method and apparatus for electromagnetic modification of brain activity |
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DE10215115A DE10215115A1 (de) | 2002-04-05 | 2002-04-05 | Verfahren und Vorrichtung zur Prävention epileptischer Anfälle |
DE10215115.6 | 2002-04-05 | ||
DE10234676.3 | 2002-07-30 | ||
DE10234676A DE10234676A1 (de) | 2002-07-30 | 2002-07-30 | Verfahren und Vorrichtung zur gesteuerten oder geregelten Modifikation von Hirnaktivität |
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AU2003221553A1 (en) | 2003-10-20 |
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