Quantitative analysis method for similarity and consistency of EEG (electroencephalogram) signals

Abstract

The invention provides an EEG (electroencephalogram) signal similarity and consistency quantitative analysis method which comprises the following steps: S1, performing segmentation processing on EEG signals acquired by two EEG electrodes to form a plurality of segment signals; s2, calculating a power spectrum density value of a key wave band in each section of signal, forming a power time sequence by a plurality of power spectrum density values, and enabling the electroencephalogram signal of each EEG electrode to correspond to a group of power time sequences; s3, comparing the power time sequences of the two electroencephalogram signals under the same wave band one by one by adopting cosine similarity measurement to obtain the cosine similarity of each wave band; s4, distributing specific weight coefficients for different wavebands of the two electroencephalogram signals; s5, accumulating the cosine similarity and the weight coefficient of each wave band of the two electroencephalogram signals to obtain a comprehensive index; the similarity or consistency level of the electroencephalogram signals collected by the two EEG electrodes can be reflected by the comprehensive indexes; according to the method, the accuracy and comprehensiveness of EEG signal similarity and consistency analysis are improved, and the analysis result is more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) signal processing, and in particular to a method for quantitatively analyzing similarity and consistency of EEG signals. Background Art

[0002] In neuroscience and clinical research, similarity and consistency analysis of EEG signals is of great significance for understanding brain activity, diagnosing neurological diseases, and evaluating treatment effects. Existing technologies mainly evaluate the similarity between signals by calculating the coherence, correlation, or power spectral density of EEG signals. However, these methods often ignore the specificity of different bands in EEG signals and their differences in contribution to the overall similarity, resulting in inaccurate and incomplete analysis results. Therefore, a new method is needed that can comprehensively consider the characteristics of different bands and accurately quantify the similarity and consistency of EEG signals. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method for quantitatively analyzing the similarity and consistency of EEG signals, which solves the problems mentioned in the background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0005] The method for quantitatively analyzing the similarity and consistency of EEG signals includes the following steps:

[0006] S1, segmenting and processing the EEG signals collected by the two EEG electrodes to form multiple segment signals;

[0007] S2, calculating the power spectrum density value of the key band in each segment signal, multiple power spectrum density values ​​form a power time series, and the EEG signal of each EEG electrode corresponds to a set of power time series;

[0008] S3, using the cosine similarity metric, the power time series of the two EEG signals in the same band are compared one by one to obtain the cosine similarity of each band;

[0009] S4, assigning specific weight coefficients to different bands of the two EEG signals;

[0010] S5. The cosine similarity and weight coefficient of each band of the two EEG signals are accumulated to obtain a comprehensive index; the comprehensive index can reflect the similarity or consistency level of the EEG signals collected by the two EEG electrodes.

[0011] Furthermore, in S1, the length of each segment signal is adjustable, including 1 second and 2 seconds.

[0012] Further, in S2, a method for calculating the power spectrum density value of the key band in each segment signal includes a fast Fourier transform method; the key band includes an Alpha wave and a Low-Beta wave.

[0013] Furthermore, in S4, the weight coefficients are allocated according to the importance or research focus of each band in EEG signal analysis.

[0014] Furthermore, the calculation formula of the cosine similarity is:

[0015]

[0016] In the above formula, the EEG signals collected by the two EEG electrodes are marked as X and Y respectively, and the power spectral density values ​​in the i-th band at time t are X and Y respectively. i (t) and Y i (t), T is the length of each signal segment, and the cosine similarity C i .

[0017] Furthermore, the calculation formula of the comprehensive index is:

[0018]

[0019] In the above formula: weight coefficient w i , comprehensive index S, N is the number of bands.

[0020] The device for quantitatively analyzing EEG signal similarity and consistency comprises a processor and a memory; the memory is used to store programs; the processor executes the programs to implement any of the above methods.

[0021] A computer-readable storage medium storing a program, wherein the program is executed by a processor to implement any of the above methods.

[0022] The present invention provides a method for quantitatively analyzing the similarity and consistency of EEG signals. Compared with the prior art, it has the following beneficial effects:

[0023] 1. Improved the accuracy and comprehensiveness of EEG signal similarity and consistency analysis. By comprehensively considering the characteristics of different bands and their contribution to the overall similarity, the analysis results are more accurate and reliable.

[0024] 2. It provides more sophisticated tools for neuroscience research and clinical applications, which helps to deeply understand the mechanism of brain activity, diagnose neurological diseases, and evaluate the effectiveness of treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of the working steps of the EEG signal similarity and consistency quantitative analysis method of the present invention is shown. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] In order to solve the technical problems in the background technology, the following EEG signal similarity and consistency quantitative analysis method is given:

[0029] Combination Figure 1 As shown, the EEG signal similarity and consistency quantitative analysis method provided by the present invention comprises the following steps:

[0030] S1, segmenting and processing the EEG signals collected by the two EEG electrodes to form multiple segment signals;

[0031] S2, calculating the power spectrum density value of the key band in each segment signal, multiple power spectrum density values ​​form a power time series, and the EEG signal of each EEG electrode corresponds to a set of power time series;

[0032] S3, using the cosine similarity metric, the power time series of the two EEG signals in the same band are compared one by one to obtain the cosine similarity of each band;

[0033] S4, assigning specific weight coefficients to different bands of the two EEG signals;

[0034] S5. The cosine similarity and weight coefficient of each band of the two EEG signals are accumulated to obtain a comprehensive index; the comprehensive index can reflect the similarity or consistency level of the EEG signals collected by the two EEG electrodes.

[0035] The above scheme has the following technical effects:

[0036] 1. Improved the accuracy and comprehensiveness of EEG signal similarity and consistency analysis. By comprehensively considering the characteristics of different bands and their contribution to the overall similarity, the analysis results are more accurate and reliable.

[0037] 2. It provides more sophisticated tools for neuroscience research and clinical applications, which helps to deeply understand the mechanism of brain activity, diagnose neurological diseases, and evaluate the effectiveness of treatment.

[0038] In this embodiment, in S1, the length of each segment signal is adjustable. The signals collected by the two EEG electrodes are processed in segments, and the length of each segment signal can be set according to research needs, such as 1 second, 2 seconds, etc.;

[0039] In this embodiment, in S2, the calculation method for calculating the power spectrum density value of the key band in each segment signal includes the fast Fourier transform method; the key band includes Alpha wave and Low-Beta wave. The power spectrum density value of each key band (such as Alpha wave, Low-Beta wave, etc.) in each segment signal is calculated by using the fast Fourier transform method to form a power time series.

[0040] In this embodiment, in S4, the weight coefficient is allocated according to the importance or research focus of each band in the EEG signal analysis.

[0041] In this embodiment, the calculation formula of the cosine similarity is:

[0042]

[0043] In the above formula, the EEG signals collected by the two EEG electrodes are marked as X and Y respectively, and the power spectral density values ​​in the i-th band at time t are X and Y respectively. i (t) and Y i (t), T is the length of each signal segment, and the cosine similarity C i The cosine similarity measurement formula was used to compare the power time series of the two electrodes in the same band one by one to obtain the cosine similarity of each band.

[0044] In this embodiment, the calculation formula of the comprehensive index is:

[0045]

[0046] In the above formula: weight coefficient w i, comprehensive index S, N is the number of bands. According to the research focus or domain knowledge, specific weight coefficients are assigned to different bands. For example, the Alpha wave may be more important for some studies, so it can be given a higher weight; the weighted cosine similarity of each band is accumulated to obtain a comprehensive index S, which is used to reflect the similarity or consistency level of the two EEG electrode signals. This comprehensive index can be used as a basis for subsequent analysis, diagnosis or evaluation.

[0047] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function; whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this article;

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here;

[0049] In the several embodiments provided herein, it should be understood that the disclosed systems, devices and methods can be implemented in other ways; for example, the device embodiments described above are only illustrative, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed; in addition, the mutual coupling or direct coupling or communication connection shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection;

[0050] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this article;

[0051] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit; the above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units;

[0052] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium; based on such an understanding, the technical solution of this article is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of this article; and the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes;

[0053] Specific embodiments are used in this article to illustrate the principles and implementation methods of this article. The description of the above embodiments is only used to help understand the methods and core ideas of this article. At the same time, for general technicians in this field, according to the ideas of this article, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on this article.

Claims

1. A method for quantitatively analyzing similarity and consistency of EEG signals, characterized in that: The steps include: S1, segmenting and processing the EEG signals collected by the two EEG electrodes to form multiple segment signals; S2, calculating the power spectrum density value of the key band in each segment signal, multiple power spectrum density values ​​form a power time series, and the EEG signal of each EEG electrode corresponds to a set of power time series; S3, using the cosine similarity metric, the power time series of the two EEG signals in the same band are compared one by one to obtain the cosine similarity of each band; S4, assigning specific weight coefficients to different bands of the two EEG signals; S5. The cosine similarity and weight coefficient of each band of the two EEG signals are accumulated to obtain a comprehensive index; the comprehensive index can reflect the similarity or consistency level of the EEG signals collected by the two EEG electrodes.

2. The method for quantitatively analyzing EEG signal similarity and consistency according to claim 1, characterized in that: In S1, the length of each segment signal is adjustable, including 1 second and 2 seconds.

3. The method for quantitatively analyzing EEG signal similarity and consistency according to claim 1, characterized in that: In S2, a method for calculating a power spectrum density value of a key band in each segment signal includes a fast Fourier transform method; the key band includes an Alpha wave and a Low-Beta wave.

4. The method for quantitatively analyzing EEG signal similarity and consistency according to claim 1, characterized in that: In S4, the weight coefficients are allocated based on the importance of each band in EEG signal analysis or research focus.

5. The method for quantitatively analyzing EEG signal similarity and consistency according to claim 1, characterized in that: The calculation formula of the cosine similarity is: In the above formula, the EEG signals collected by the two EEG electrodes are marked as X and Y respectively, and the power spectral density values ​​in the i-th band at time t are X and Y respectively. i (t) and Y i (t), T is the length of each signal segment, and the cosine similarity C i .

6. The method for quantitatively analyzing EEG signal similarity and consistency according to claim 5, characterized in that: The calculation formula of the comprehensive index is: In the above formula: weight coefficient w i , comprehensive index S, N is the number of bands.

7. EEG signal similarity and consistency quantitative analysis device, characterized by: It comprises a processor and a memory; the memory is used to store a program; the processor executes the program to implement the method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 6.

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