EP1931251A2

EP1931251A2 - Method and arrangement for the analysis of a time-variable bioelectromagnetic signal

Info

Publication number: EP1931251A2
Application number: EP06791304A
Authority: EP
Inventors: Alfred Scherbaum; José Alberto GONZALÈZ-HERNANDÈZ
Original assignee: Heinrich Heine Universitaet Duesseldof
Current assignee: Heinrich Heine Universitaet Duesseldof
Priority date: 2005-08-04
Filing date: 2006-08-04
Publication date: 2008-06-18
Also published as: US20090221929A1; WO2007014558A2; WO2007014558A3; DE112006002659A5

Abstract

The invention relates to a method and an arrangement for the analysis of a time-variable bioelectromagnetic signal that has been recorded over a defined space of time. Said signal is split into at least two signal components differing in terms of the frequency ranges thereof, by means of a bandpass filter. At least one first type of reference moment is defined according to pre-determinable selection criteria in at least one of the signal components differing in terms of the frequency ranges thereof, and the values of at least two signal components differing in terms of the frequency ranges thereof are interrelated at the defined reference moment according to pre-determinable evaluation criteria.

Description

METHOD AND ARRANGEMENT FOR ANALYZING A TIMELY CHANGING BIOELECTROMAGNETIC SIGNAL

Background of the invention

The invention relates to a method and an arrangement for analyzing a time-varying bioelectromagnetic signal. In particular, the invention relates to a method and an arrangement for analyzing electroencephalographic signals. In independent claims relates to

Invention advantageously trained electroencephalographies, electromyographers, Magnetoenzephalographen and Elektroneurographen.

In this case, "bioelectromagnetic signals" are understood as meaning electrical and / or magnetic signals detectable by means of corresponding sensors and detectors, such as electrodes, which originate in the electrical activity of a biological object such as, for example, a beating heart or another muscle Brain or peripheral nerves. Since moving electrical charges induce magnetic fields, the following is always spoken of electromagnetic signals, even if in many applications actually only an electrical potential or its change over time is measured.

State of the art

The acquisition and evaluation of bioelectromagnetic signals has gained in importance in recent years and is used in a variety of fields, not only in research and medicine, application, for example in the control of machines by people without the use of muscle power. Other examples include the creation of intelligent prostheses which respond to electromagnetic pulses emanating from the user's brain and perform certain actions. From DE 43 27 429 A1 a method for brainwave analysis is known in which the signals detected at the head of a human are divided into signal components of different frequency ranges by means of an analog or digital bandpass filter. It has been shown that in the brain between the neurons electrical impulses with clearly distinguishable from each other

Frequencies are exchanged, wherein the different frequency ranges commonly referred to as α-, ß-, γ- and Θ-areas. It is generally known e.g. the so-called oc state when the main brain activity occurs at frequencies in the α-range and the human is in a relaxed state in which he is particularly adaptive.

From DE 693 30 644 T2 a method for the separation of signal components of a time-varying multi-channel measuring signal is known, wherein it is in the measuring signal, for example. evoked electrical and magnetic response signals, spontaneous activity signals from the brain or from the heart

Measuring signals can act.

From DE 198 19497 A1 a device for the identification of heart and brain conditions due to different frequency spectral structures of the electromagnetic activities of these organs enervating neurons is known in which the electromagnetic activities are repeatedly detected and fed to an electrical device, which receives the received electromagnetic signals from a Time spectrum transferred to a frequency level.

From DD 267 335 A1 a circuit arrangement for analyzing an electroencephalogram is known, with which the accuracy of electroencephalograms and thus their diagnostic value should be increased.

From DD 299 509 A7 a method for event-related nonlinear topological function analysis is known, which is able to determine dynamically non-linear functionally relevant topological parameters. DE 692 28 823 T2 discloses a method for the non-invasive detection of cerebral phenomena in which dynamic phase relations are characterized after bandpass filters of electroencephalographic signals within the filtered signals.

In addition, from the scientific literature (eg Duffy, FH: Topography Mapping of Brain Electric Activity, Boston, Butterworth 1986, 7-28) different methods for the resolution of impulse responses to external stimuli and their areal representation, the so-called "mapping", known eg of evoked brain electrical potentials.

Disclosure of the invention

In the known methods and arrangements for the analysis of bioelectromagnetic signals, there is the problem of so-called "biological referencing", that is the relationship of the measured signals to suitable reference points for the purpose of obtaining relevant information, e.g. for controlling a machine, as a statement about the effect of drugs or as a diagnostically relevant parameter, the basis for the later

Evaluation by a doctor can be.

Conventionally, to obtain relevant information from the measured signals, the signals to empirically derived data, e.g. typical averages, for example, by checking whether a measured value lies within a typical value range. However, bioelectromagnetic signals are naturally subject to some noise and are individually different in strength and severity, so that it is often difficult to obtain meaningful information solely by comparison with reference data.

Based on this, the object of the invention is a method and an arrangement for analyzing a time-varying bioelectromagnetic signal, which detects over a certain time interval and be split by means of a band-pass filtering in at least two different with respect to their frequency ranges signal components to specify, which allow information in a simple manner, such as control information for the control of a machine, a prosthesis or the like. or to gain diagnostically meaningful parameters.

In procedural terms, the object is achieved by a method in which at least one reference time of the first type is determined in at least one of the signal components different in their frequency ranges and the values of at least two signal components differing in their frequency ranges at the specific reference time of the first type predetermined evaluation criteria are related to each other.

The invention is based on the surprising finding that important information can be obtained from bioelectromagnetic signals when the bandpass filtered signals are related to each other by considering the behavior of certain signal components at characteristic reference times, but the reference times are not "externally" predetermined but be determined according to specifiable selection criteria from the detected bioelectromagnetic signal itself.

As selection criteria, those skilled in the art will advantageously benefit from criteria adapted to the particular signal to be analyzed, such as the

Exceeding certain predefinable thresholds. In a preferred embodiment of the method it is provided that the step of determining a reference time in a signal component comprises determining any extreme values and / or inflection points which may be present in the time interval in the signal component. Many bioelectromagnetic

Signals, such as the signal measured by means of an electrocardiograph, have characteristic curve shapes whose sections can be easily identified by means of turning points within a cycle (eg the so-called QRS complex or the ST segment in the electrocardiogram or the general as P and N peaks (P as positive, N as negative) in the evoked potential curve in electroencephalography).

After determining at least one first reference instant of the first type in a first signal component according to the same selection criteria, at least one second reference instant of the first type is determined in at least one second signal component which differs from the first with respect to its frequency range, whereupon the values of at least two signal components differing with regard to their frequency ranges be related to each other in the first and second reference times of the first kind. For example, if the signal under study is the detection of the change in potential evoked in response to a simple physical or cognitive stimulus, as evidenced by electroencephalographs, a functional analysis of the signal may be obtained, e.g. The question is how individual neural networks, whose neurons use waves of a certain frequency to communicate with each other, behave at certain times, to which networks whose neurons with "send other frequencies" are, for example, particularly active. This will be discussed in the context of the description of an embodiment.

In a further preferred embodiment, the step of relating the values assumed by different signal components in the reference time (s) of the first kind comprises determining differences between the ones

Signal components and / or determining tendencies such as rising or falling in the individual signal components. Such differences and tendencies can be excellently visualized with visualization methods known per se, if necessary after the solution of the so-called "inverse problem", so that certain information can clearly emerge and be easily read.

If the bioelectromagnetic signal to be analyzed is split into N (N e N ₊ , with N ₊ = set of integer natural numbers greater than 0) different signal components differing in their frequency ranges, then so be proceeded that at least one reference time of the first kind is determined in each of the N signal components according to the same selection criteria. Advantageously, at least one N × N matrix of the first type can then be created, in which the values of each signal component are entered at the N reference times of the first kind and from which different functional and temporal ones

Information can be read easily. This will also be discussed below.

If the bioelectromagnetic signal to be analyzed has been recorded by means of a multichannel acquisition device in such a way that it is possible to analyze the signal with regard to the spatial distribution of its sources in the bioelectromagnetically active object being examined, it can advantageously be determined in which regions of the object under investigation one of the determined Reference points a particular, in the generation of a characteristic change of a certain of the signal components differing in their frequency ranges resulting activity was present. It can also be determined which regions of the examined object are active at the reference time determined with respect to a signal component for generating signal components of other frequency ranges.

In an advantageous embodiment of the method according to the invention it is provided that at least one reference time of at least one second type, preferably a second and a third type is determined. This allows, for example, different characteristic events to be selected as reference times. In encephalographic potentials evoked by a visual stimulus, for example, the time points at which brainwaves in the α, β and γ frequency ranges, which propagate at different speeds in the brain, each have their own so-called C-peak, can be selected as reference times of the first type , as reference times of the second kind, the times at which brain waves in the α-, ß- and γ-frequency range, the respective P-peaks, and as reference times of the third kind, the times at which the brain waves in α-, ß- and γ-frequency range Reach N-tips. If reference times of different types have been selected, then advantageously the step of relating at least two signal components differing with respect to their frequency ranges can also take place for the reference times of the second and possibly third type, and not necessarily to those for the reference time (s) first type identical predetermined evaluation criteria.

The method has proven to be particularly advantageous for use with such bioelectromagnetic signals that occur in response to an external stimulus supplied to the bioelectromagnetic signal generating the bioelectromagnetic active object. In this case, it is possible to proceed in such a way that the stimulus is repeatedly supplied to the object, that correspondingly several times a bioelectromagnetic signal is detected and that the signal to be analyzed is finally formed from suitable averaging of the detected signals. In particular, such averaging methods are considered, the specific

Disregard "outliers" when determining an averaged signal.

If the bioelectromagnetic signal to be analyzed was an electroencephalogram recorded by a multichannel electroencephalograph, good results were obtained using simple cognitive stimuli, especially simple visual stimuli such as e.g. achieved a changing with a certain frequency checkerboard pattern, which was shown to a person to be examined achieved.

Arrangementally, the object is achieved by an arrangement for analyzing a time-varying bioelectromagnetic signal detected over a certain time interval comprising an analog or digital band-pass filter for splitting the signal into at least two signal components different in their frequency ranges, Means for automatically determining at least one reference time of the first type in at least one of the different with respect to their frequency ranges

Signal components and means for automatically relating the values of at least two signal components differing with respect to their frequency ranges in the determined reference time of the first kind. In an advantageous embodiment, the means for determining a reference time can be designed such that they enable the determination of extreme values and / or turning points which may be present in a considered signal component.

Preferably, the means for determining a reference time are designed such that they allow the determination of a plurality of reference times of the same or different type in different signal components differing with regard to their frequency ranges.

The means for automatically relating the values of at least two signal components different in their frequency ranges may be arranged to allow the values of any signal components different in their frequency ranges to be related at reference times of any kind.

Further, the means for relating the values of different signal components at the reference time (s) may be arranged to determine the differences between the signal components and / or to determine trends such as increasing or decreasing in the individual ones

Enable signal components.

The bandpass filter may be arranged to split the bioelectromagnetic signal into N (N e N ₊ ) signal components different in frequency ranges, preferably N being at least equal

3, more preferably equal to 5 or 6.

If the bandpass filter permits splitting into N frequency ranges, the arrangement expediently comprises at least one memory unit into which at least one N × N matrix of the first type can be written, which contains the values of each signal component at N reference times of the same type.

Was the bioelectromagnetic signal recorded by means of a multi-channel acquisition device such that an analysis of the signal in terms of spatial Distribution of its sources in a studied bioelectromagnetically active object is possible, the arrangement can advantageously means for determining and / or visualization of the regions of the examined object, in which one of the specific reference times a particular, in the production of a particular differing in terms of their frequency ranges

Signal components resulting activity include.

The above-mentioned means for determining and / or visualizing the regions of the examined object in which a specific activity is present at one of the determined reference times can be designed such that they enable the regions of the examined object to be identified and / or visualized with respect to a signal component specific reference time for the generation of signal components of other frequency ranges are active.

The independent claims 27 to 30 each relate to an advantageously designed electroencephalographies, electromyographs, magnetoencephalographs and electroneurographs. The subordinate claim 31 relates to a machine-readable memory containing the commands required for automatically carrying out a method according to the invention.

Further details and advantages of the invention will become apparent from the following purely exemplary and non-limiting description of embodiments in conjunction with the drawings.

Brief description of the figures

1 shows a schematic diagram which illustrates the state of the art (a) in comparison with a basic idea of the invention (b) using the example of an electroencephalographically measured, visually evoked potential. Fig. 2 is a schematic diagram for illustrating the classical course of the electroencephalographic measurement of a visually evoked potential.

Fig. 3 shows in four fields a) to d) purely schematically the basic

Method steps for obtaining the electroencephalographic data of visually evoked potentials according to the invention.

4 shows, purely schematically, the selection of reference points of the first, second and third types and of the relationship of the values of the different signal components differing with respect to their frequency ranges at the reference times.

Fig. 5 shows purely schematically the structure of an N x N matrix according to an advantageous embodiment of the invention.

FIG. 6 shows purely schematically, using the example of electroencephalographic data, some of the information obtainable after the inverse problem has been solved, namely according to the prior art (FIG. 6a) and according to the method according to the invention (FIG. 6b).

Fig. 7 shows the example of electroencephalographic data in the form of

Sectional images of the present invention recoverable information (7b) compared to the prior art (Fig. 7a).

Description of preferred embodiments

In the following, the invention is explained using the example of the application to electroencephalographically measured data.

Fig. 1 shows schematically the procedure for obtaining such electroencephalographic data by visual evocation of potentials in the brain. This will be a subject about a certain Time zone shown a checkerboard pattern in which at a certain frequency, typically 1 Hz, the fields swap their colors, so black fields white and white fields are black.

This simple visual stimulus evokes a potential in the brain of the subject and thus a bioelectric signal in the sense of the invention, which can be measured with a conventional electroencephalography, for example. 30 Ag / AgCI electrodes are placed at various points of the subject's skull using the international 10/10 electrode placement system.

As already mentioned above, the neurons in the brain form various neural networks (indicated by the reference numeral 10 in Fig. 1), using waves of different frequencies substantially in a range between 0.5 and 70 Hz for communication with each other. As already mentioned, this frequency range is usually subdivided into specific subregions, so that one can designate the individual networks according to the frequencies of the waves used by their neurons for communication among themselves as γ, α, β, β1 and Θ networks , In the signal measured with the electrodes, the various components can be isolated by bandpass filtering.

Classically, however, these different networks are not taken into account in the evaluation of the measured data: as indicated in a) in FIG. 1, an averaging is carried out over the entire measured values to form a single graph, in which the course of the measured potentials over the Time is plotted and in which the marked turning points are commonly referred to as P and N peaks and numbered starting with 1 (P1, N1, etc.). The graph of the graph is generally roughly roughly such that approximately 70 ms after the stimulus, a first significant extreme value in the negative

Potential range is (in Fig. 1 denoted by "C1"), about 100 ms after the stimulus, the first peak in the positive range (P1) and about 140 ms after the stimulus, the so-called N 1 peak is in the negative potential range. After solving the so-called "inverse problem", ie the calculation from the data measured on the surface of the skull, where a potential has been evoked in the brain, a pictorial representation as shown in FIG. 1 can be provided, for example in the form of an incision or a virtual three-dimensional image of the skull and brain, and then the area in which the strongest on average

Activity took place in which image is highlighted graphically.

Surprisingly, it has now been shown that important information can be obtained if the networks mentioned are considered separately and their behavior is responded to in response to e.g. the said visual stimulus are related to each other at characteristic reference times. As indicated in FIG. 1 under b), a consideration of the individual components in the signal measured by the electrodes takes place by splitting into the different frequency ranges, so that e.g. shows five or six graphs or a graph with five or six potential gradients, which were designated in Fig. 1 under b) with γ, γ [40], α, ß, ß1 and θ and, as also in Fig. 1 under b), allow identification of the frequencies of distinguishable networks used by their neurons (indicated by reference numeral 12 in FIG. 1).

Since the differences between the γ, γ [40] networks are small, the γ [40] network was not suggested in the scheme.

FIG. 2 shows the classical course of the electroencephalographic measurement of a visually evoked potential. Fig. 2a shows the two different

Checkerboard pattern A and B, which, as indicated in Fig. 2b, alternately e.g. at a frequency of 1 Hz to an eye of an examined person, in the example of Fig. 2b the left eye.

This stimulus creates an electrical activity on the so-called visual

Cortex (labeled "VC" in Figure 2b) of the subject's brain. Typically, after averaging over a period of time, eg 60 seconds, this electrical activity is converted to a graph as shown in Figure 2c. FIG. 3 shows, in four partial diagrams, purely schematically the basic method steps for obtaining the electroencephalographic data then considered according to the invention.

The checkerboard pattern already described is shown to a person not shown here (FIG. 3a).

This visual stimulus generates an electrical activity in the brain of the person being examined, that is to say the neurons of different neural networks are activated, and they oscillate to communicate with each other

Use frequency (schematically indicated in Fig. 3b).

This activity, which can be measured in a manner known per se by means of a conventional electroencephalography on the outside of the skull, is now bandpass filtered (FIG. 3c) so that the electroencephalographic signal can be split into different signal components with respect to the different frequency ranges as indicated in FIG ,

Splitting the measured signal into the different signal components makes it possible to identify different networks in the brain. Surprisingly, it has now been found that important information, e.g. for the control of a machine, a prosthesis and the like, but in particular also diagnostically meaningful parameters, can be obtained if the various signal components are related to each other, and not, as hitherto customary, to any comparison group, which is shown in FIG 4 is indicated.

For this purpose, in the individual signal components γ, γ [40], α, β, β1 and θ, first certain reference times, in the exemplary embodiment shown reference time of the first, second and third type, are selected, namely

Reference times of the first type in each case the points in time at which the signal components reach the C1 peaks, as reference times of the second type in each case the points in time at which the signal components reach the P1 peaks and as reference time of the third kind, the times at which the signal components each reach the N1 peaks.

It should be emphasized at this point that, of course, depending on the nature of the stimulus (visual, acoustic, olfactory, gustatory, tactile, etc.) and type of measurement

(electroencephalographically, electrocardiographically, etc.) and depending on the task completely different reference times can be selected. It is important that reference times are selected within the various signal components at all, to which then the behavior of the other signal components is considered, so the values of their

Frequency range differing signal components in the selected reference times are set according to specifiable evaluation criteria to each other. Such evaluation criteria may be the determination of differences between the signal components and / or the determination of tendencies such as rising or falling in the individual signal components.

Then, as shown schematically in Fig. 5, an N x N matrix can be created in which the diagonal is the reference time of the same kind, e.g. the P1 peaks, represented. On the diagonal of the matrix, therefore, the values of those signal components are entered, which at the respective time just their own

Reach P 1 peak. In the rows of the matrix, the values of each of a signal component which it has at the times, to itself or the other signal components, for example, are entered. reach the P1 tip. In the columns of the matrix are entered the values of the various signal components which they have at the time when one of the signal components, e.g. reaches the P1 peak. This gives a spatio-temporal resolution, the peculiarity is that the reference times are dynamic, so do not lie in any fixed time intervals, but just dynamically selected whenever a signal component, for example. goes through her own P1 tip. In addition, since the other signal components are connected to this respective signal component in

Relationship, and not to external comparison data, one can speak of a "dynamic self-referencing". The matrix contains two types of information: one describing the temporary evolution of each network, the other showing the contextual interaction of the networks at specific times. Both data have different functional implications that can be shown by statistical dependencies.

By solving the aforementioned inverse problem and corresponding visualization techniques, as shown in FIG. 6 of course only two-dimensionally, virtual 3D images can be constructed which contain valuable information. Classically, after averaging across all signal components, one would obtain only an image as shown in Fig. 6a, in which the averaged behavior of all the different networks at a given reference time, e.g. the P1 tip is shown.

On the other hand, if the signal components e.g. in five different

Splitting frequency ranges and looking at the signal components at the reference times selected as described above, one obtains, as shown in Fig. 6b, 25 pictures which visualize the behavior of the various networks at different reference times. In this case, in Fig. 6b is marked with the black triangle in each case the network that just a

Reference time, e.g. the P1 tip, delivers. Horizontally next to it the temporal development of the same network is readable, namely at the times, at which other networks go through the respective P1-peak.

Surprisingly, it has been shown that certain pathological or by

Medications caused changes in the behavior of the networks can not be determined in the classical way, since after averaging and In reference to external reference data no deviation was found, while using the dynamic self-referencing certain disease patterns or certain drugs significant differences in the spatial effect temporal development of the various network activities, so that, for example, in a person suffering from a particular disease or even for such a disease assessed persons at a time when a particular network passes through the P1 peak, already another network in becomes active in a certain area, while such activity does not show in healthy persons. This is impressively illustrated in FIGS. 7a and 7b: while sick and healthy persons showed no significant deviation in an evaluation of electroencephalographically obtained data according to the prior art (FIG. 7a), it permits the invention

Evaluation of the same data to identify significantly different activations in different areas in patients with different diseases (Fig. 7b).

It has also been shown that not only the spatial, but also the temporal

Behavior of the various networks can contain revealing information, obtained by the invention for the first time and further evaluation of e.g. can be made accessible by a doctor. Thus, it has been shown that under certain test conditions between healthy and sick patients, although no deviation in the spatial activation of certain areas in the brain, but a difference in the temporal behavior of the networks can be found at the dynamic reference time points.

The invention thus advantageously also allows screening tests and early detection and can also in the development and in particular the test of new

Medicines can be used to advantage.

It should be emphasized that the invention also new business procedures, in particular the sale of analyzes of bioelectromagnetic, in particular electroencephalographic signals implied, these methods are hereby expressly designated as belonging to the invention and in those countries whose national law so permits.

Claims

claims

A method of analyzing a time varying bioelectromagnetic signal which has been detected over a certain time interval, the signal being split by means of band pass filtering into at least two signal components different in frequency ranges, characterized in that in at least one of at least one reference time of the first kind is determined according to predetermined selection criteria and that the values of at least two signal components differing with respect to their frequency ranges are related to one another in the specific reference time of the first type according to specifiable evaluation criteria.

2. The method according to claim 1, characterized in that the step of determining a reference time in a signal component comprises determining in the time interval in the signal component optionally present extreme values and / or inflection points.

3. The method of claim 1 or 2, characterized in that at least a first reference time of the first kind is determined in a first signal component, - that according to the same selection criteria in at least one of the first with respect to their frequency range different second signal component at least a second reference time of the first kind is determined and that the values of at least two signal components differing in their frequency ranges are related to one another in the first and second reference times of the first type.

4. The method according to any one of claims 1 to 3, characterized in that the step of interrelated setting of the values of different signal components in the reference time or points (s) of the first type, the determination of differences between the signal components and / or the detection of tendencies such as rising or falling in the individual signal components.

5. The method according to any one of claims 1 to 4, wherein N (N e N ₊ ) are considered with respect to their frequency ranges signal components of a bioelectromagnetic signal, characterized in that in each of the N signal components according to the same selection criteria at least one reference time of the first kind is determined ,

6. The method according to claim 5, characterized in that at least one N x N matrix of the first kind is created, in which the values of each signal component at the N reference times of the first kind are entered.

7. The method according to any one of claims 1 to 6, wherein the bioelectromagnetic signal has been recorded by means of a multi-channel acquisition device such that an analysis of the signal in terms of the spatial distribution of its sources in a studied bioelectromagnetically active object is possible, characterized in that it is determined in which regions of the object under investigation at one of the specific reference times have a specific activity resulting in the generation of a specific one of the signal components that differ with respect to their frequency ranges.

8. The method according to claim 7, characterized in that it is determined which regions of the examined object are active at the reference time determined with respect to a signal component for generating signal components of other frequency ranges.

9. The method according to any one of claims 1 to 8, characterized in that at least one reference time of at least one second type, preferably a second and a third type is determined.

10. The method according to claim 9 and one of claims 1 to 8, characterized in that the steps of the relationship of setting at least two differing in terms of their frequency signal components also for the reference times of the second and optionally third type according to not necessarily identical predetermined Evaluation criteria take place.

11. The method according to any one of claims 1 to 10, characterized in that the bioelectromagnetic signal to be analyzed is a signal generated in response to the bioelectromagnetic signal generating the bioelectromagnetically active object supplied external stimulus signal.

12. The method according to claim 10, characterized in that the bioelectromagnetic signal to be examined is a signal resulting from the averaging of the signals supplied after multiple supply of the stimulus.

13. The method according to any one of claims 1 to 12, characterized in that the bioelectromagnetic signal is recorded by means of a multichannel electroencephalograph electroencephalogram.

14. The method according to claim 13 and one of claims 11 or 12, characterized in that the stimulus is a physical / cognitive stimulus, in particular a visual, a tactile, a gustatory, an olfactory and / or an acoustic stimulus.

15. The method according to claim 14, characterized in that the stimulus is a changing with a certain frequency checkerboard pattern.

A method according to any one of claims 13 to 15, wherein the signal is a signal resulting in response to a stimulus, in particular a physical / cognitive stimulus such as a visual stimulus, with extremes commonly referred to as P and N peaks, thereby in that the time points at which the signal components reach the P peaks are selected as the reference time of the first type, and in each case the points in time at which the signal components reach the N peaks are selected as the reference time of the second type.

17. The method according to any one of claims 1 to 16, characterized in that the signal to be analyzed is split by means of a band-pass filtering at least in three, preferably in five to six frequency ranges.

18. Arrangement for analyzing a time-varying bioelectromagnetic signal which has been detected over a certain time interval, comprising - an analogue or digital bandpass filter for splitting the signal into at least two signal components differing in their frequency ranges, characterized in that

Means for automatically determining at least one reference time of the first type in at least one of the signal components differing with respect to their frequency ranges and

Means are provided for automatically relating the values of at least two signal components differing with respect to their frequency ranges in the particular reference time of the first type.

19. The arrangement according to claim 18, characterized in that the means for determining a reference time are designed such that they allow the determination in a considered signal component optionally present extreme values and / or turning points.

20. Arrangement according to claim 18 or 19, characterized in that the means for determining a reference time are designed such that they determine the determination of a plurality of reference times of the same or different kind in different with respect to their frequency ranges

Enable signal components.

21. Arrangement according to one of claims 18 to 20, characterized in that the means for automatically relating the values at least two signal components differing in their frequency ranges are designed such that they allow the values of any signal components differing in their frequency ranges to be related in reference times of any kind.

An arrangement according to any one of claims 18 to 21, characterized in that the means for relating the values of different signal components at the reference time (s) are arranged to determine the differences between the signal components and / or identifying trends such as rising or falling in the individual

Enable signal components.

23. Arrangement according to one of claims 18 to 22, characterized in that the bandpass filter is designed such that it differs the bioelectromagnetic signal in N (N e N ₊ ) with respect to their frequency ranges

Signal components can split, wherein N is preferably at least equal to 3, more preferably equal to 5 or 6.

24. Arrangement according to claim 23, characterized in that a memory unit is provided, in which at least one N x N matrix of the first kind can be written, which contains the values of each signal component at N reference times of the same kind.

25. Arrangement according to one of claims 18 to 24, wherein the bioelectromagnetic signal was recorded by means of a multi-channel acquisition device such that an analysis of the signal with respect to the spatial distribution of its sources in a bioelectromagnetically active object is possible, characterized in that means for determining and / or visualization of the regions of the examined object in which there is a specific activity resulting in the generation of a specific one of the signal components differing with regard to their frequency ranges for one of the determined reference times.

26. Arrangement according to claim 25, characterized in that the means for determining and / or visualizing the regions of the examined object in which there is a specific activity for one of the determined reference times are designed such that they determine and / or visualize the Regions of the examined object allow, with respect to a

Signal component specific reference time for the generation of signal components of other frequency ranges are active.

27. Elektroenzephalograph, characterized in that it comprises an arrangement according to one of claims 18 to 26 and / or means for carrying out a

A method according to any one of claims 1 to 17.

28. An electromyograph, characterized in that it comprises an arrangement according to one of claims 18 to 26 and / or means for carrying out a method according to one of claims 1 to 17.

29. An electroneurograph, characterized in that it comprises an arrangement according to one of claims 18 to 26 and / or means for carrying out a method according to one of claims 1 to 17.

30. Magnetoencephalograph, characterized in that it comprises an arrangement according to one of claims 18 to 26 and / or means for carrying out a method according to one of claims 1 to 17.

31. A machine-readable memory containing the commands required to automatically perform a method according to any one of claims 1 to 17.

32. Method for facilitating the sale of analyzes of bioelectromagnetic signals, in particular electroencephalographic signals, comprising carrying out a method according to one of claims 1 to 17.