Methods and apparatus for determining microstates in electroencephalograms

Abstract

A method and device for determining microstates of an electroencephalogram (EEG) are proposed. The method includes acquiring electroencephalogram (EEG) signals of a subject on multiple lead channels; clustering the electroencephalogram (EEG) signals based on reference microstates corresponding to normal electroencephalogram (EEG) signals; and generating a microstate template corresponding to the subject based on the clustering results to determine the microstates. The method can reduce the strict requirements on the quality of the collected EEG signals, reduce the sensitivity of the clustering analysis method to noise in the EEG signals, and maintain the accuracy and efficiency of the microstate analysis method.

Description

Background Art

[0002] Electroencephalogram (EEG) signals can characterize human brain electrical activity, and they have the advantages of high temporal resolution and high spatial resolution. In 1987, Lehmann first proposed the concept of microstates based on the topological changes in the brain topography of EEG signals. He believes that the microstates of EEG can reflect that the changes in the global brain function state of humans are discontinuous and nonlinear. Microstates reveal that 90% of the changes in human brain activity can be explained by the transitions between four microstates, and the transitions between these four microstates can represent any changes in human brain activity. In 2009, Lehmann further determined experimentally that the duration of an EEG microstate is 80-120ms, and that microstates cannot smoothly transition to each other, but change suddenly.

[0003] EEG microstates are an increasingly popular method for analyzing EEG activity. Usually, a batch of EEG signal data from subjects is used to apply clustering methods such as k-means to obtain the above four types of microstates, and then further study brain function on the conversion relationship between microstates and the number of times each microstate occurs.

[0004] However, most existing microstate analysis schemes are based on clustering EEG signal data after strict denoising to obtain four microstates. For single EEG signal data, due to the different EEG signal quality, especially when there is high noise in the EEG signal, resulting in poor signal quality, the existing microstate clustering method's demand for high signal quality makes it more sensitive to noise and less applicable, and cannot guarantee the expected clustering effect.

[0005] Therefore, there is a need to improve existing EEG microstate determination and analysis schemes. Summary of the invention

[0006] In view of the defects and problems to be solved mentioned above, the present application proposes a scheme for determining the microstates of the electroencephalogram (EEG), which can overcome the sensitivity of existing microstate clustering analysis techniques to noise (especially high noise components) in EEG signals, and embodies the advantage of accurately determining specific microstates for subjects based on EEG signals under various noise conditions.

[0007] According to one aspect of the present application, a method for determining electroencephalogram (EEG) microstates is proposed, comprising: acquiring EEG signals of a subject on multiple lead channels; clustering the EEG signals based on reference microstates corresponding to normal EEG signals; and generating a microstate template corresponding to the subject based on the clustering results to determine the microstates.

[0008] According to another aspect of the present application, a device for determining electroencephalogram (EEG) microstates is proposed, comprising: an acquisition unit configured to acquire EEG signals of a subject on multiple lead channels; an analysis unit configured to cluster the EEG signals based on reference microstates corresponding to normal EEG signals, and to generate a microstate template corresponding to the subject based on the clustering results to determine the microstates.

[0009] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program includes executable instructions. When the executable instructions are executed by a processor, the method described above is implemented.

[0010] According to another aspect of the present application, an electronic device is provided, comprising a processor; and a memory for storing executable instructions of the processor; wherein the executable instructions implement the method described above when executed by the processor.

[0011] Based on the method and device for determining the microstate of the electroencephalogram (EEG) proposed in this application, the strict requirements for the quality of the collected EEG signal can be reduced, the sensitivity of the cluster analysis method to the noise in the EEG signal can be reduced, and the accuracy and efficiency of the microstate analysis method can be maintained. Among them, by using the microstate clustering results of normal EEG signal data from a large number of healthy people as the universal typical form of the reference microstate, and replacing the original random clustering center in the traditional k-means clustering method with it, the iteration speed of the clustering algorithm can be accelerated. In the iterative process, the iterative process is further fine-tuned by the clustering center backoff and the number of iterations, and the speed and degree of the clustering results deviating from the original clustering center are controlled to ensure that each iteration will not deviate too far from the reference clustering center. This method reduces the dependence of the microstate clustering and analysis method on the signal quality as much as possible under the premise of ensuring that the microstate clustering results at least reach or even exceed the results of the traditional k-means clustering method, and can also reflect the specificity of the microstate for the subject. On the basis of improving the clustering process, the present application can also screen the samples participating in the clustering, further reduce and / or remove the noise interference caused by excessively high noise components in the collected EEG signals or by insufficiently strict denoising, and improve the accuracy of the clustering results of the microstates. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0013] Figure 1 The present invention is a schematic flowchart of a method for determining microstates of an electroencephalogram (EEG) according to an embodiment of the present application.

[0014] Figure 2It is a schematic diagram of extracting samples to be clustered from electroencephalogram (EEG) signals according to one embodiment of the present application.

[0015] Figure 3 Schematic diagram of a microstate topographic map of a reference microstate as a fixed cluster center according to an embodiment of the present application.

[0016] Figure 4 It is a schematic structural block diagram of a device for determining microstates of an electroencephalogram according to an embodiment of the present application.

[0017] Figure 5 It is a schematic block diagram of an electronic device for determining microstates of an electroencephalogram according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided to make the contents of this application comprehensive and complete and to fully convey the concepts of the exemplary embodiments to those skilled in the art. In the drawings, the size of some elements may be exaggerated or deformed for clarity. The same reference numerals in the drawings represent the same or similar structures, and their detailed descriptions will be omitted.

[0019] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, elements, etc. can be adopted. In other cases, known structures, methods or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0020] In clinical practice, electroencephalogram (EEG) devices (e.g., electroencephalogram (EEG) amplifiers) are used to collect EEG signals of patients. In this article, the microstate of the electroencephalogram (EEG) mainly refers to the microstate of the subject in a resting state, which generally includes four microstates of right frontal-left posterior (A), left frontal-right posterior (B), middle frontal-occipital (C), and middle frontal (D) topographic maps. The microstate time series is used to characterize the change of microstates over time, and its commonly used microstate parameters can be the average duration, frequency of occurrence, coverage, topographic map (topological shape map), microstate amplitude, global explained variance, and transition probability of microstates generated from EEG signals. These parameter changes can describe the microstate changes of the human brain. In the exemplary embodiment below, a specific microstate corresponding to the subject is obtained based on a distribution map of the signal voltage amplitude on multiple lead channels of the EEG signal as a microstate topographic map. However, those skilled in the art will understand that the microstate topographic map in the form of a voltage amplitude distribution map is only an example and not a limitation to the scheme of the present application. Those skilled in the art can also use other microstate parameters to implement the determination and analysis scheme of the microstate of EEG proposed in the present application.

[0021] As mentioned above, the existing microstate clustering analysis scheme has high requirements on the signal quality of EEG signals (i.e., low-noise EEG signals must be used), which makes it impossible to apply to EEG signals with a high proportion of noise components. EEG signals with high noise content will cause the microstate topography (i.e., clustering samples) involved in clustering to be uneven, so the microstates obtained through the clustering results cannot represent the true state of human brain activity. How to reduce or remove the impact of noise components in EEG signals on clustering results, and then more quickly and accurately determine the microstate template based on the clustering results to determine the microstate specific to the individual subject, is the focus of the scheme of this application.

[0022] The following is combined with Figure 1 The system flow for determining the microstate of the electroencephalogram (EEG) according to an embodiment of the present application is introduced.

[0023] Figure 1 The method for determining microstates shown mainly includes step S110 of acquiring EEG signal data, step S120 of optionally preprocessing the EEG signal, step S130 of determining samples to be clustered, step S140 of clustering, step S150 of generating a microstate template based on the clustering results, and step S160 of finally determining the microstate of the subject according to the microstate template.

[0024] In step S110, EEG signals on multiple lead channels from the EEG device are obtained. According to clinical standards, an EEG amplifier can be used to collect the EEG signals of the subject in the awake and closed-eye resting state. The sampling rate can be above 200 Hz and the number of lead channels can be above 16. Figure 2 201 of the EEG signal waveform is shown with multiple lead channels, such as FP1, FP2, F3, F4, C3, C4, P4, O1, F7, T3, FZ, CZ and PZ, etc. The EEG signals on the multiple lead channels are synchronously recorded in the same EEG signal waveform diagram on the horizontal axis of time (in seconds or milliseconds, etc.) for comparison and further processing.

[0025] In the optional step S120, the EEG signal is preprocessed. The purpose of the preprocessing operation is to reduce the amount of data calculation to improve the calculation efficiency, eliminate the influence of invalid data and interference on the detection, and improve the resolution and accuracy of the EEG signal waveform.

[0026] The preprocessing operation step S120 may include resampling the EEG signal, removing the EEG signal on the invalid lead channel, filtering to remove at least one of noise such as power frequency noise, baseline drift noise and / or high frequency noise, and calibrating the EEG signal based on a reference signal (re-referencing).

[0027] Resampling the EEG signal is used to improve computational efficiency. The resampling frequency must be at least twice the sampling frequency of the EEG amplifier, generally above 150 Hz, for example, 200 Hz as described above, to ensure detection accuracy. Resampling can be achieved by downsampling or by upsampling through interpolation.

[0028] The purpose of removing EEG signals on invalid lead channels is to filter out the signals of the recorded lead channels that are no longer needed in the later steps, such as the EEG signals of the lead channels of the bilateral mastoid points used as references. The criterion for determining whether the EEG signals of the lead channels are needed can be whether the lead channels have effectively collected EEG signals.

[0029] Filtering to remove noise is used to avoid the negative impact of various interference signals on detection. These noises include power frequency noise, baseline drift noise and / or high-frequency noise. Power frequency noise is generally caused by the natural frequency of the power system. In my country, the operating frequency of AC power is 50Hz (or 60Hz). Power frequency noise will interfere with the normal operation of electronic equipment and may cover the effective information contained in the EEG signal. A notch filter with an operating frequency of 50Hz and / or a multiple of 50Hz can be designed to eliminate power frequency noise interference in EEG signals. Filters can also be designed to remove baseline drift noise and / or high-frequency noise in the signal. Baseline drift mainly comes from changes in circuit component parameters caused by physical characteristics of the channel sensor and environmental factors (such as changes in ambient temperature), which results in the superposition of components unrelated to the measured signal in the signal input. High-frequency noise comes from electromyographic interference and environmental noise. Existing research results show that the alpha frequency band (8-12Hz) of resting-state EEG signals of multiple lead channels can be parsed into several different quasi-steady states. Taking into account the influence of low-frequency and high-frequency noise and the differences in the alpha frequency band range of subjects of different ages, this application selects a bandpass filter whose operating frequency range corresponds to a wider alpha frequency band range, such as a frequency band range of 4.5-13Hz, to filter and denoise the EEG signal, so as to facilitate the determination and analysis of microstates.

[0030] Re-reference is used to calibrate the EEG signal based on the reference signal, wherein the EEG signal detected on the global multiple lead channels is subtracted from the reference signal to eliminate the common error present in the signal on all lead channels. For example, the EEG signal on the lead channel of the left forehead and the EEG signal obtained on the lead channel of the right forehead both have a common error signal, then re-reference can remove the error signal shared by the two. Re-reference can include re-reference algorithms such as single electrode re-reference (monopolarrereference), bipolar re-reference (bipolar rereference), Laplace re-reference (laplacianrereference) or global re-reference (common average rereference). In this article, global re-reference is used as an example to introduce the method of the present application, that is, the average value of all EEG signal data of the whole brain is used as reference data. The waveform amplitude value of the acquired EEG signal at each sampling moment is subtracted from the data of the reference signal value at the sampling moment, and the EEG signal after re-reference can be obtained.

[0031] After the preprocessing of the EEG signal is completed, in step S130, the samples to be clustered are determined by the general field power (GFP) curve.

[0032] Step S130 may further include a sub-step S131 of generating a GFP curve by calculating the GPF value of the EEG signal on all lead channels at each moment after signal preprocessing, and a sub-step S132 of extracting samples for clustering based on a microstate topographic map composed of corresponding microstate parameters on all lead channels at the moment corresponding to the GFP peak value (e.g., maximum value) in the curve.

[0033] The measured value of GFP reflects the potential intensity measured by the electrode on the subject's head skin, which can be calculated by the following formula (1):

[0034]

[0035] Where GFP(t) is the total field power of the EEG signal on all lead channels at time t. n is the number of electrodes in these lead channels. Since the potential difference between the electrode pair including the reference electrode and the test electrode is used to measure the voltage on each lead channel, n is actually the number of lead channels. i represents each electrode of interest, which is a positive integer and 1≤i≤n. Based on the corresponding relationship between the number of electrodes and the number of lead channels, i can also represent the lead channel of interest. V i (t) is the voltage of the EEG signal on the i-th lead channel measured at time t, V mean (t) is the average voltage of the EEG signal on all multiple lead channels measured at time t.

[0036] The GFP curve calculated based on formula (1) is Figure 2 As shown in 202, it can be seen that the GFP value is also a signal curve related to time. After the GFP curve is generated in sub-step S131, the samples for participating in clustering are extracted in sub-step S132 according to the corresponding relationship between the GFP value and the microstate represented by the EEG signal.

[0037] The value of GFP is related to the shape of the stable EEG signal. The signal-to-noise ratio is the ratio of the output signal power of the amplifier to the noise power output at the same time. A stable EEG signal has a higher signal-to-noise ratio, so the value of GFP is also related to the signal-to-noise ratio at the EEG signal end. In other words, a higher GFP value corresponds to a higher EEG signal-to-noise ratio. The EEG signal corresponding to the GFP at the maximum value has the highest signal-to-noise ratio. The relevant microstate parameters of the EEG signal corresponding to the GFP value at this point can more accurately characterize the microstate at the highest signal-to-noise ratio, and the corresponding microstate topography is also relatively more accurate.

[0038] The GFP curve can have multiple local maxima (i.e., multiple peaks on the curve), so finding the peak on the GFP curve can locate the stable EEG signal morphology at or near the peak. GFP(t) is a time-dependent signal curve or sequence. For the EEG signal on each lead channel, the time t corresponding to the (local) maximum value of GFP (max(GFP(t)) can be calculated, that is, the time t when GFP reaches the peak value. For example, the instantaneous voltage Vi(t) of the EEG signal on each lead channel at time t is selected to generate a distribution map of the instantaneous voltage amplitude at time t as a microstate topography. See Figure 2 , taking the partial curve 202-1 in the GFP curve 202 calculated from the original or preprocessed EEG signal as an example, the corresponding moments of the 11 extreme points (peak points) in the GFP curve can be found. At the moment t where each extreme point is located, there is a corresponding set of distribution diagrams of the instantaneous voltage amplitude of the EEG signal on each lead channel, that is, the microstate topographic map 203, which are represented by 203-1, 203-2, 203-3, ..., 203-11 respectively. That is, each GFP extreme point or peak point corresponds to a microstate that can be represented by the microstate topographic map 203. The details of the microstate topographic map are described in detail below.

[0039] The purpose of using the microstate topography determined by the extreme values ​​or peaks of the GFP curve as clustering samples is to cluster these microstates into four typical microstates that can characterize the brain activity of the subject.

[0040] After determining the samples to be clustered, the method executes a clustering algorithm in step S140 .

[0041] Step S140 may further include a sub-step S141 for determining a fixed cluster center based on a reference microstate corresponding to a normal EEG signal, a sub-step S142 for screening samples based on the similarity between the samples and the fixed cluster center before clustering the samples, and a sub-step S143 for fine-tuning the iterative cluster center during the clustering process.

[0042] According to the traditional microstate analysis method proposed by Lehmann, when clustering samples using a clustering method including k-means, a random clustering center is usually used to obtain the microstate template of the subject from the EEG signal, thereby further obtaining brain activity information. The accuracy of the result of this clustering algorithm is very dependent on the quality of the original EEG signal. According to an embodiment of the present application, in step S141, compared with the traditional scheme using a random clustering center, a fixed clustering center is used to implement the clustering operation. The fixed clustering center can not only speed up the iteration speed of the clustering algorithm, but also reduce the degree of dependence of the clustering effect on the signal quality, especially reduce the adverse effect of the quality of the EEG signal with high noise components on the clustering results. The fixed clustering center is determined based on the reference microstate 101. The reference microstate 101 comes from the EEG signals of multiple normal people. These normal human brain EEG signals are strictly denoised and then obtained using the clustering operation in the traditional microstate analysis method. The (four) microstates used as reference microstates 101 are morphological templates of universal typical microstates of human brain EEG signals, and the microstate templates of individual subjects have some specific fine-tuning based on the reference microstates 101. The typical microstate morphology corresponding to normal human brain EEG signals has been manually verified, and its theoretical analysis can be found in the following literature: Khanna A, Pascual-Leone A, Farzan F. Reliability of resting-state microstate features in electroencephalography [J]. PloS one, 2014, 9 (12): e114163, and da Cruz JR, Favrod O, Roinishvili M, et al. EEG microstates are a candidate endophenotype for schizophrenia [J]. Nature communications, 2020, 11 (1): 1-11.

[0043] Figure 3 The details of the microstate topography of the reference microstate 101 are shown, and the reference microstate comes from the EEG signal of a normal person on multiple (e.g., 19) lead channels. Among them, after the EEG signal is processed by denoising and traditional clustering methods, the microstate topography of four microstates drawn according to the position distribution of the microstate parameter values ​​on the top view of the brain area is obtained, which are marked as MS0 to MS3. Figure 3In the example, the electrode voltage of the lead channel is used as the microstate parameter. Different colors and depths (scales) are used to indicate the magnitude and positive and negative of the voltage amplitude. For example, red indicates positive voltage and blue indicates negative voltage. The darker the color, the larger the absolute value of the voltage amplitude, and the lighter the color, the smaller the absolute value of the voltage amplitude. In the microstate topographic map marked as MS0, the left posterior brain region of the human body is dark red, and its voltage amplitude is the largest (reaching about 0.50V). The voltage amplitude gradually decreases toward the right forebrain region until the minimum voltage amplitude (about -0.50V) represented by dark blue. The microstate topographic maps of the other three microstates marked by MS1 to MS3 present different voltage amplitude distributions. For example, the area with large voltage amplitude in the microstate topographic map marked as MS2 is concentrated near the center of the human brain. Figure 2 The microstate topography 203 of the sample to be clustered determined based on the peak point of the GFP curve shown in Figure 3 The microstate template of each subject finally obtained by the clustering operation of the embodiment of the present application is based on Figure 2 The microstate topography shown in is iteratively transformed into a new personalized microstate template.

[0044] The specific fine-tuning when determining the microstate template of a single individual includes not only using the fixed cluster center as the original cluster center for the clustering operation, but also screening the samples to be clustered and controlling the iteration process through the backoff method and the maximum number of iterations. This fine-tuning can reduce the dependence on the quality of the EEG signal while ensuring the accuracy of the microstate clustering results.

[0045] In sub-step S142, the samples may be screened before clustering. The screening may be performed based on the similarity between the samples participating in the clustering and the original cluster center (i.e., the fixed cluster center determined above). The similarity may be determined, for example, by the following similarity calculation formula (2):

[0046]

[0047] Among them, sim is the similarity between the sample and the fixed cluster center, v is the fixed cluster center, and vi is the sample participating in the clustering, that is, the voltage amplitude distribution measured on multiple lead channels of the EEG signal participating in the clustering at the time corresponding to the peak point of GFP. norm() means to find the modulus length of the vector of the microstate topographic map, in this example, it is to find the modulus length of the voltage amplitude distribution. Finally, the absolute value of the fraction is taken as the similarity sim. Here, the voltage amplitude distribution is a vector in the form of a topographic map, such as Figure 2 203 and Figure 3 As shown in MS0 to MS3.

[0048] Vector multiplication v*vi can be calculated as the sum of the products of the voltage amplitudes of the EEG signals on the same lead channels in the two microstate topographic maps. The modulus norm operation in the denominator can be performed by taking the L2 norm of the microstate topographic map vector so that the modulus units of the two vectors calculated in the denominator are consistent. In this way, the product of the moduli in the denominator can be calculated by scalar multiplication. It can be understood that the quotient of the vector product in the numerator divided by the product of the vector moduli in the denominator is equivalent to normalizing the vector product, so that the calculation of the similarity value between the microstate topographic maps of the sample and the fixed cluster center does not consider the size of the voltage amplitude of the EEG signal (i.e., the microstate parameter) but only considers the similarity of the microstate topographic maps. The value of the quotient finally calculated should be in the range of [0,1].

[0049] The similarity screening criterion may be that the calculated similarity value is greater than or equal to a preset similarity threshold 102. The similarity threshold 102 is obtained through strict verification. According to an embodiment of the present application, the similarity threshold 102 is related to the degree to which the clustering result depends on the signal quality of the EEG signal, and more specifically, to the degree to which the clustering result can reduce the dependence of the clustering result on the signal quality of the EEG signal. Under the premise of ensuring that the explanation rate of the clustering result can be as close to the explanation rate of the traditional k-means clustering method as possible, the similarity that can minimize the dependence on the EEG signal quality is selected as the similarity threshold 102 by cyclically verifying the values ​​of each similarity.

[0050] The purpose of the sub-step S142 of selecting samples is to select the microstate topographic map at the time point with high similarity to the fixed cluster center to participate in the subsequent iterative steps in the clustering operation, so as to reduce and / or remove the noise point interference caused by excessive noise components in the collected EEG signal or insufficient denoising, and improve the accuracy of the microstate clustering results. This is one of the obvious differences between the present application and traditional methods such as k-means and random clustering.

[0051] Specific fine-tuning when determining the microstate template of a single individual may also include an operation of retracting a new cluster center at each iteration of the clustering process based on the mobility threshold 103 in sub-step S143 and / or an operation of controlling the number of iterations based on the iteration number threshold 104.

[0052] After the samples are screened in sub-step S142, a new cluster center is iterated by the clustering algorithm, wherein the average value of the microstate topographic map corresponding to all samples in each microstate category is used as the new cluster center of the category. After each iteration, the new cluster center can be moved to the original cluster center (i.e., the fixed cluster center), for example, linearly. Here, this movement of the new cluster center to the original cluster center is called the (linear) backoff operation of the vector. The degree of backoff can be a preset backoff moving distance or a preset distance percentage of the backoff moving distance to the distance from the new cluster center to the original cluster center. The preset distance or the preset distance percentage is called the moving degree threshold 103. The purpose of the backoff operation is to control the iteration speed to prevent the final clustering result from deviating too far from the original fixed cluster center. The larger the moving degree threshold 103 of the backoff operation, the slower the iteration speed, and the smaller the degree to which the new cluster center deviates from the original cluster center in the clustering process.

[0053] The degree of iteration can also be controlled by setting an iteration number threshold 104. It can be set to stop iteration when the number of iterations reaches the iteration number threshold 104, instead of judging the timing of stopping iteration based on the convergence condition of the iteration in the traditional (k-means) clustering method.

[0054] Both the movement degree threshold 103 and the iteration number threshold 104 are related to the degree of dependence of the clustering result on the signal quality of the EEG signal, and more specifically, to the degree to which the clustering result can reduce the dependence of the clustering result on the signal quality of the EEG signal. Under the premise of ensuring that the interpretation rate of the clustering result can be as close to the interpretation rate of the traditional k-means clustering method as possible, the movement degree and iteration number that can minimize the dependence on the signal quality can be selected as the movement degree threshold 103 and the iteration number threshold 104 by cyclically verifying each threshold.

[0055] After considering the specific fine-tuning of the microstate template of a single individual in the clustering process, a clustering result is obtained. In step S150, a personalized microstate template corresponding to the subject is generated based on the clustering result.

[0056] In step S160, the microstate template is further used to obtain the microstate of the subject for brain activity analysis. Since the obtained microstate template is individually fine-tuned for each subject, a more accurate microstate of each subject can be obtained, thereby improving the accuracy and effectiveness of microstate analysis.

[0057] Figure 4A device 400 for determining the microstate of EEG according to an embodiment of the present application is shown. The device 400 includes at least an acquisition unit 410 for acquiring EEG signal data of a subject on multiple lead channels and an analysis unit 420 for clustering the EEG signal based on a reference microstate corresponding to a normal EEG signal, and generating a microstate template corresponding to the subject based on the clustering result to determine the microstate.

[0058] The analysis unit 420 can also perform the following Figure 1 The step S120 of preprocessing the signal can be completed after the acquisition unit 410 acquires the EEG signal data, or can be completed by the analysis unit 420 before generating the GFP curve.

[0059] Combined with the above Figure 1 Similar contents of the introduced method steps will not be described in detail.

[0060] The device 400 may also include an output unit (not shown) for outputting the clustering results or the determined microstate templates, microstates, and microstate analysis results to the user. The output unit may, for example, provide visual and / or audible information to the user using a display, a touch screen, etc. The device 400 may also provide a communication unit to transmit information such as the clustering results to other devices in a remote location via a wired or wireless manner.

[0061] Based on the method and device for determining the microstate of the electroencephalogram (EEG) proposed in this application, the strict requirements for the quality of the collected EEG signal can be reduced, the sensitivity of the cluster analysis method to the noise in the EEG signal can be reduced, and the accuracy and efficiency of the microstate analysis method can be maintained. Among them, by using the microstate clustering results of normal EEG signal data from a large number of healthy people as the universal typical form of the reference microstate, and replacing the original random clustering center in the traditional k-means clustering method with it, the iteration speed of the clustering algorithm can be accelerated. In the iterative process, the iterative process is further fine-tuned by the clustering center backoff and the number of iterations, and the speed and degree of the clustering results deviating from the original clustering center are controlled to ensure that each iteration will not deviate too far from the reference clustering center. This method reduces the dependence of the microstate clustering and analysis method on the signal quality as much as possible under the premise of ensuring that the microstate clustering results at least reach or even exceed the results of the traditional k-means clustering method, and can also reflect the specificity of the microstate for the subject. On the basis of improving the clustering process, the present application can also screen the samples participating in the clustering, further reduce and / or remove the noise interference caused by excessively high noise components in the collected EEG signals or by insufficiently strict denoising, and improve the accuracy of the clustering results of the microstates.

[0062] It should be noted that although several modules or units of the device and system for determining the microstate of the electroencephalogram (EEG) are mentioned in the above detailed description, this division is not mandatory. In fact, according to the implementation mode of the present application, the features and functions of the two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of a module or unit described above can be further divided into multiple modules or units to be concretized. The components displayed as modules or units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the present application. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0063] In an exemplary embodiment of the present application, a computer-readable storage medium is also provided, on which a computer program is stored, the program including executable instructions, which can implement the steps of the method for determining the microstate of the electroencephalogram (EEG) described in any of the above embodiments when executed by, for example, a processor. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to cause the terminal device to execute the steps of various exemplary embodiments of the present application described in the method for determining the microstate of the electroencephalogram (EEG) in this specification.

[0064] The program product for implementing the above method according to the embodiment of the present application can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this article, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0065] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0066] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0067] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0068] In an exemplary embodiment of the present application, an electronic device is also provided, which may include a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the steps of the method for determining the microstate of the electroencephalogram (EEG) in any of the above embodiments by executing the executable instructions.

[0069] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0070] Refer to the following Figure 5 The electronic device 500 according to this embodiment of the present application is described. Figure 5 The electronic device 500 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0071] like Figure 5 As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510), a display unit 540, etc.

[0072] The storage unit stores a program code, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps of various exemplary embodiments of the present application described in the method for determining the microstate of the electroencephalogram (EEG) in this specification. For example, the processing unit 510 can perform the following steps: Figure 1 Follow the steps shown in .

[0073] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 5201 and / or a cache memory unit 5202 , and may further include a read-only memory unit (ROM) 5203 .

[0074] The storage unit 520 may also include a program / utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0075] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0076] The electronic device 500 may also communicate with one or more external devices 600 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 550. Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 560. The network adapter 560 may communicate with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0077] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the method for determining the microstate of the electroencephalogram EEG according to the implementation of the present application.

[0078] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and embodiments are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the appended claims.

Claims

1. A method for determining microstates of an electroencephalogram, comprising: Acquiring the subject's electroencephalogram signals on multiple lead channels; Clustering the electroencephalogram signal based on a reference microstate corresponding to a normal electroencephalogram signal, the clustering comprising: generating a global field power curve of the electroencephalogram based on the acquired electroencephalogram signal; taking the microstate topography of the electroencephalogram signal on the multiple lead channels at the time corresponding to the peak of the overall field power curve as the sample of the clustering; Clustering the samples based on the reference microstates, wherein the microstate topographic map of the reference microstates is used as a fixed clustering center, and before clustering the samples, screening the samples based on the similarity between the samples and the fixed clustering center; A microstate template corresponding to the subject is generated based on the clustering result to determine the microstate.

2. The method according to claim 1, characterized in that The total field power is calculated by the following formula: Wherein, GFP(t) is the total field power of the EEG signal at time t, n is the number of lead channels, i is a positive integer and 1≤i≤n, V i (t) is the voltage of the EEG signal of the ith lead channel measured at time t, V mean (t) is the average value of the voltage of the electroencephalogram signals of the multiple lead channels measured at time t.

3. The method according to claim 2, characterized in that The reference microstates are determined by strictly denoising and clustering normal EEG signals.

4. The method according to claim 1, characterized in that: The similarity is calculated by the following formula: Among them, sim is the similarity between the sample and the fixed cluster center, v is the fixed cluster center, vi is the sample participating in the clustering, and norm() represents the modulus length of the vector of the microstate topographic map.

5. The method according to claim 1, characterized in that Screening the sample based on the similarity between the sample and the fixed cluster center includes: The samples whose similarity exceeds a similarity threshold are selected to participate in the clustering, wherein the similarity threshold is related to the degree to which the clustering result depends on the signal quality of the electroencephalogram signal.

6. The method according to claim 1, characterized in that In the iterative process of clustering, after a new cluster center is generated in each iteration, the new cluster center is moved toward the fixed cluster center.

7. The method according to claim 6, characterized in that The new cluster center is moved toward the fixed cluster center based on a movement degree threshold and / or the number of iterations is controlled based on an iteration number threshold, wherein the movement degree threshold and the iteration number threshold are related to the degree to which the clustering result depends on the signal quality of the electroencephalogram signal.

8. The method according to any one of claims 1 to 7, characterized in that The microstate topographic map is a voltage amplitude distribution map of the electroencephalogram signal on the multiple lead channels.

9. The method according to any one of claims 1 to 7, characterized in that Before clustering the electroencephalogram signals, the electroencephalogram signals are subjected to a preprocessing operation including at least one of the following: resampling the electroencephalogram signal; Remove EEG signals on invalid lead channels; Removing power frequency noise, baseline drift noise and / or high frequency noise in the electroencephalogram signal; The electroencephalogram signal is calibrated based on a reference signal.

10. An apparatus for determining electroencephalographic microstates, comprising: An acquisition unit, configured to acquire an electroencephalogram signal of a subject on a plurality of lead channels; The analysis unit is configured as follows: Clustering the electroencephalogram signal based on a reference microstate corresponding to a normal electroencephalogram signal, the clustering comprising: generating a global field power curve of the electroencephalogram based on the acquired electroencephalogram signal; taking the microstate topography of the electroencephalogram signal on the multiple lead channels at the time corresponding to the peak of the overall field power curve as the sample of the clustering; Clustering the samples based on the reference microstates, wherein the microstate topographic map of the reference microstates is used as a fixed clustering center, and before clustering the samples, screening the samples based on the similarity between the samples and the fixed clustering center; and A microstate template corresponding to the subject is generated based on the clustering result to determine the microstate.

11. A computer-readable storage medium having a computer program stored thereon, the computer program comprising executable instructions, and when the executable instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

12. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, when the executable instructions are executed by the processor, the method according to any one of claims 1 to 9 is implemented.

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