Methods, systems, electronic devices, and storage media for recording electroencephalogram (EEG) signals.

Abstract

This application provides a method, system, electronic device, and storage medium for recording electroencephalogram (EEG) signal data, comprising: acquiring an EEG signal to be recorded from target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses; statistically analyzing the values ​​of the EEG signal to be recorded to obtain EEG signal characteristics of the target EEG data; classifying and labeling the EEG signal to be recorded according to the EEG signal characteristics, wherein the classification includes: normal EEG signals and abnormal EEG signals; and classifying and recording the normal EEG signals and / or abnormal EEG signals. This allows EEG signals to be divided into normal and abnormal EEG signals based on EEG waveforms, and records corresponding index addresses for different types of EEG signals, significantly shortening the retrieval time for EEG signals.

Description

Technical Field

[0001] This application belongs to the field of electroencephalography (EEG), and particularly relates to methods, systems, electronic devices, and storage media for recording EEG signal data. Background Technology

[0002] Currently, the main method for recording EEG signals is multichannel signaling. Multichannel signaling has a limited number of signal channels and recording time, and mostly uses raw data recording, resulting in a small amount of recorded data. Therefore, the amount of data for analysis is also small. When searching for data, the method of traversing the data to find the required data is mostly used, rather than using an index to search for data.

[0003] The search time for EEG signals using a traversal method is greatly affected by the amount of data. When the amount of data is small, the search time is short and acceptable. However, if the amount of data increases, it will lead to a longer search time for the required data and consume a lot of resources. Summary of the Invention

[0004] The main objective of this invention is to provide a method, system, electronic device, and storage medium for recording electroencephalogram (EEG) signal data, which allows EEG signals to be classified into normal and abnormal EEG signals based on EEG waveforms, and records the corresponding index addresses for different types of EEG signals, thereby greatly shortening the retrieval time of EEG signals.

[0005] In a first aspect, a method for recording electroencephalogram (EEG) signal data is provided, the method comprising:

[0006] Acquire the EEG signal to be recorded from the target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses;

[0007] The numerical values ​​of the EEG signals to be recorded are statistically analyzed to obtain the EEG signal characteristics of the target EEG data;

[0008] The recorded EEG signals are classified and labeled according to the characteristics of the EEG signals, and the classification includes: normal EEG signals and abnormal EEG signals;

[0009] The normal and / or abnormal EEG signals are classified and recorded.

[0010] In one possible implementation, the classification and recording of the normal and / or abnormal EEG signals includes:

[0011] If the EEG signal to be recorded is a normal EEG signal, then record the start time, peak / trough frequency, duration, and index address of the normal EEG signal; and,

[0012] If the EEG signal to be recorded is an abnormal EEG signal, then record the start time, frequency of peaks or troughs, duration, and index address of the abnormal EEG signal.

[0013] In another possible implementation, the step of statistically analyzing the values ​​of the EEG signals to be recorded and obtaining the EEG signal features of the target EEG data includes:

[0014] The number of peaks and / or troughs in the EEG signals to be recorded within a set time period is counted, and the value of each peak and / or trough is obtained.

[0015] Based on the stated quantity and value, obtain the average peak value and / or average trough value.

[0016] In another possible implementation, classifying and labeling the EEG signal to be recorded based on the EEG signal features includes:

[0017] Obtain the peak value and / or trough value of the EEG signal to be recorded, and the peak value difference and / or trough value difference between the average peak value and / or average trough value;

[0018] If the absolute values ​​of the peak difference and / or trough difference are outside the threshold range, the EEG signal to be recorded is an abnormal EEG signal; and if the absolute values ​​of the peak difference and / or trough difference are within the threshold range, the EEG signal to be recorded is a normal EEG signal.

[0019] Secondly, a system for recording electroencephalogram (EEG) signal data is provided, the system comprising:

[0020] The EEG signal acquisition module is used to acquire the EEG signal to be recorded from the target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses.

[0021] The EEG signal feature acquisition module is used to statistically analyze the values ​​of the EEG signals to be recorded and acquire the EEG signal features of the target EEG data.

[0022] A classification and labeling module is used to classify and record the normal and / or abnormal EEG signals, wherein the classification includes: normal EEG signals and abnormal EEG signals;

[0023] The index address recording module is used to record the corresponding index addresses for the normal EEG signals and / or abnormal EEG signals respectively.

[0024] In one possible implementation, the classification and recording of the normal and / or abnormal EEG signals includes:

[0025] If the EEG signal to be recorded is a normal EEG signal, then record the start time, peak / trough frequency, duration, and index address of the normal EEG signal; and,

[0026] If the EEG signal to be recorded is an abnormal EEG signal, then record the start time, frequency of peaks or troughs, duration, and index address of the abnormal EEG signal.

[0027] In another possible implementation, the EEG signal feature acquisition module includes:

[0028] The quantity and value acquisition unit is used to count the number of peaks and / or troughs in the EEG signal to be recorded, and to acquire the value of each peak and / or trough.

[0029] The average value acquisition unit is used to acquire the average peak value and / or average trough value based on the quantity and value.

[0030] In another possible implementation, the classification tagging module includes:

[0031] The numerical difference acquisition unit is used to acquire the peak value difference and / or trough value difference between the peak value and / or trough value of the EEG signal to be recorded and the average peak value and / or average trough value.

[0032] The classification unit is configured to classify the EEG signal to be recorded as an abnormal EEG signal if the absolute value of the peak difference and / or trough difference is greater than a preset value difference threshold; and to classify the EEG signal to be recorded as a normal EEG signal if the absolute value of the peak difference and / or trough difference is less than a preset value difference threshold.

[0033] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for recording electroencephalogram (EEG) signal data as provided in the first aspect.

[0034] Fourthly, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method for recording electroencephalogram (EEG) signal data as provided in the first aspect. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0036] Figure 1 A flowchart illustrating a method for recording electroencephalogram (EEG) signal data according to an embodiment of the present invention;

[0037] Figure 2 A flowchart illustrating a method for recording electroencephalogram (EEG) signal data according to another embodiment of the present invention;

[0038] Figure 3 A flowchart illustrating a method for recording electroencephalogram (EEG) signal data according to another embodiment of the present invention;

[0039] Figure 4 A structural diagram of a system for recording electroencephalogram (EEG) signal data according to an embodiment of the present invention;

[0040] Figure 5 A structural diagram of a system for recording electroencephalogram (EEG) signal data provided in another embodiment of the present invention;

[0041] Figure 6 A structural diagram of a system for recording electroencephalogram (EEG) signal data according to another embodiment of the present invention;

[0042] Figure 7 This is a schematic diagram of the physical structure of an electronic device according to the present invention.

[0043] Specific implementation method

[0044] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar modules or modules having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting the invention.

[0045] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, modules, components, and / or groups thereof. It should be understood that when we say a module is “connected” or “coupled” to another module, it can be directly connected or coupled to the other module, or there may be an intermediate module. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein includes all or any of the modules and all combinations thereof of one or more associated listed items.

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the implementation of this application will be described in further detail below with reference to the accompanying drawings.

[0047] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0048] like Figure 1 The diagram shows a flowchart of a method for recording electroencephalogram (EEG) signal data according to an embodiment of the present invention. The method includes:

[0049] Step 101: Obtain the EEG signal to be recorded from the target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses;

[0050] Step 102: Statistically analyze the values ​​of the EEG signals to be recorded to obtain the EEG signal characteristics of the target EEG data;

[0051] Step 103: Classify and label the EEG signal to be recorded according to the characteristics of the EEG signal. The classification includes: normal EEG signal and abnormal EEG signal.

[0052] Step 104: Classify and record the normal and / or abnormal EEG signals.

[0053] In this embodiment of the invention, a complete EEG data segment contains multiple segments of EEG signals with short-duration nerve impulse waveforms. At the start of recording, the EEG signals with nerve impulse waveforms can be extracted from the complete EEG data segment visually and marked as the EEG signals to be recorded. The EEG signals to be recorded are nerve impulse signals with a "peak-trough" pattern. The EEG signal characteristics of the target EEG data can be obtained by statistically analyzing the values ​​of each peak and trough. The EEG signals to be recorded are compared with the EEG signal characteristics. Based on the comparison results, the EEG signals to be recorded are classified into normal EEG signals and abnormal EEG signals. Simultaneously, the corresponding index addresses of normal and abnormal EEG signals are recorded, completing the classification and recording of the EEG signals.

[0054] The EEG signal features include: action potential threshold, waveform envelope feature points, number of peaks, number of troughs, duration, and / or power spectral density.

[0055] The classification and recording of the normal and / or abnormal EEG signals includes:

[0056] If the EEG signal to be recorded is a normal EEG signal, then record the start time, peak / trough frequency, duration, and index address of the normal EEG signal; and,

[0057] If the EEG signal to be recorded is an abnormal EEG signal, then record the start time, frequency of peaks or troughs, duration, and index address of the abnormal EEG signal.

[0058] Specifically, the classification and recording of the normal and / or abnormal EEG signals includes: creating corresponding recording information based on the normal and / or abnormal EEG signals to establish an index library, wherein each recording information includes an index segment, a data segment, and a signal type.

[0059] The index segment includes the index address of the EEG signal, which is the storage location of the EEG signal in the index database. The data segment includes the action potential threshold, waveform envelope feature points, number of peaks, number of troughs, duration, and / or power spectral density of the EEG signal. The signal type includes normal EEG signals, abnormal EEG signals, and ambiguous EEG signals. Ambiguous EEG signals refer to signals whose nature cannot currently be determined as either abnormal or normal.

[0060] In a preferred embodiment, the data segment further includes the location of the brain tissue corresponding to the electroencephalogram (EEG) signal.

[0061] In this embodiment of the invention, the EEG signals to be recorded are acquired from the target EEG data, the numerical values ​​of the EEG signals to be recorded are statistically analyzed, the EEG signal characteristics of the target EEG data are obtained, and the EEG signals to be recorded are classified and labeled as normal EEG signals and / or abnormal EEG signals according to the EEG signal characteristics. The corresponding index addresses of normal EEG signals and / or abnormal EEG signals are recorded respectively. This allows the EEG signals in the EEG data to be classified and recorded statistically, reducing the time required to retrieve EEG signals.

[0062] like Figure 2 The diagram shows a flowchart of a method for recording electroencephalogram (EEG) signal data according to another embodiment of the present invention. The step of statistically analyzing the values ​​of the EEG signal to be recorded and obtaining the EEG signal characteristics of the target EEG data includes:

[0063] Step 201: Count the number of peaks and / or troughs in the EEG signals to be recorded within the set time period, and obtain the value of each peak and / or trough.

[0064] Step 202: Based on the quantity and value, obtain the average peak value and / or average trough value. In this embodiment of the invention, the main features of the EEG signal include the average peak value and / or average trough value, which can be used as the basis for judging normal and / or abnormal EEG signals in subsequent processes.

[0065] like Figure 3The diagram shows a flowchart of a method for recording electroencephalogram (EEG) signal data according to another embodiment of the present invention. The step of classifying and labeling the EEG signal to be recorded based on its characteristics includes:

[0066] Step 301: Obtain the peak value and / or trough value difference between the peak value and / or trough value of the EEG signal to be recorded and the average peak value and / or average trough value.

[0067] Step 302: If the absolute values ​​of the peak value difference and / or trough value difference are outside the threshold range, the EEG signal to be recorded is an abnormal EEG signal; and if the absolute values ​​of the peak value difference and / or trough value difference are within the threshold range, the EEG signal to be recorded is a normal EEG signal.

[0068] In this embodiment of the invention, the type of EEG signal is mainly determined by the difference between the peak value and the average peak value of the EEG signal to be recorded, and the difference between the trough value and the average trough value. Since the peak and / or trough of the EEG signal to be recorded can be greater than or less than the average peak and / or average trough, after obtaining the peak and / or trough value differences, the absolute value of the peak and / or trough value differences is taken and compared with a preset value difference threshold. If the absolute value is greater than the value difference threshold, the EEG signal to be recorded is a normal EEG signal; if the absolute value is less than the value difference threshold, the EEG signal to be recorded is an abnormal EEG signal.

[0069] like Figure 4 The diagram shown is a structural diagram of a system for recording electroencephalogram (EEG) signal data according to an embodiment of the present invention. The system includes:

[0070] The EEG signal acquisition module 401 is used to acquire the EEG signal to be recorded in the target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses.

[0071] The EEG signal feature acquisition module 402 is used to statistically analyze the values ​​of the EEG signals to be recorded and acquire the EEG signal features of the target EEG data.

[0072] The classification and labeling module 403 is used to classify and record the normal EEG signals and / or abnormal EEG signals, wherein the classification includes: normal EEG signals and abnormal EEG signals;

[0073] The index address recording module 404 is used to record the corresponding index addresses for the normal EEG signal and / or abnormal EEG signal respectively.

[0074] In this embodiment of the invention, a complete EEG data segment contains multiple segments of EEG signals with short-duration nerve impulse waveforms. At the start of recording, the EEG signals with nerve impulse waveforms can be extracted from the complete EEG data segment visually and marked as the EEG signals to be recorded. The EEG signals to be recorded are nerve impulse signals with a "peak-trough" pattern. The EEG signal characteristics of the target EEG data can be obtained by statistically analyzing the values ​​of each peak and trough. The EEG signals to be recorded are compared with the EEG signal characteristics. Based on the comparison results, the EEG signals to be recorded are classified into normal EEG signals and abnormal EEG signals. Simultaneously, the corresponding index addresses of normal and abnormal EEG signals are recorded, completing the classification and recording of the EEG signals.

[0075] The classification and recording of the normal and / or abnormal EEG signals includes:

[0076] If the EEG signal to be recorded is a normal EEG signal, then record the start time, peak / trough frequency, duration, and index address of the normal EEG signal; and,

[0077] If the EEG signal to be recorded is an abnormal EEG signal, then record the start time, frequency of peaks or troughs, duration, and index address of the abnormal EEG signal.

[0078] In this embodiment of the invention, the EEG signals to be recorded are acquired from the target EEG data, the numerical values ​​of the EEG signals to be recorded are statistically analyzed, the EEG signal characteristics of the target EEG data are obtained, and the EEG signals to be recorded are classified and labeled as normal EEG signals and / or abnormal EEG signals according to the EEG signal characteristics. The corresponding index addresses of normal EEG signals and / or abnormal EEG signals are recorded respectively. This allows the EEG signals in the EEG data to be classified and recorded statistically, reducing the time required to retrieve EEG signals.

[0079] like Figure 5 The diagram shown is a structural diagram of a system for recording electroencephalogram (EEG) signal data according to another embodiment of the present invention. The EEG signal feature acquisition module 402 includes:

[0080] The quantity and value acquisition unit 501 is used to count the number of peaks and / or troughs in the EEG signal to be recorded, and to acquire the value of each peak and / or trough.

[0081] The average value acquisition unit 502 is used to acquire the average peak value and / or average trough value based on the quantity and value.

[0082] In this embodiment of the invention, the main features of the electroencephalogram (EEG) signal include the average peak value and / or the average trough value. The average peak value and / or the average trough value can be used as the basis for judging normal and / or abnormal EEG signals in subsequent processes.

[0083] like Figure 6 The diagram shown is a structural diagram of a system for recording electroencephalogram (EEG) signal data according to another embodiment of the present invention. The classification and labeling module 403 includes:

[0084] The numerical difference acquisition unit 601 is used to acquire the peak value difference and / or trough value difference between the peak value and / or trough value of the EEG signal to be recorded and the average peak value and / or average trough value.

[0085] The classification unit 602 is configured to classify the EEG signal to be recorded as an abnormal EEG signal if the absolute value of the peak value difference and / or trough value difference is greater than a preset value difference threshold; and to classify the EEG signal to be recorded as a normal EEG signal if the absolute value of the peak value difference and / or trough value difference is less than a preset value difference threshold.

[0086] In this embodiment of the invention, the type of EEG signal is mainly determined by the difference between the peak value and the average peak value of the EEG signal to be recorded, and the difference between the trough value and the average trough value. Since the peak and / or trough of the EEG signal to be recorded can be greater than or less than the average peak and / or average trough, after obtaining the peak and / or trough value differences, the absolute value of the peak and / or trough value differences is taken and compared with a preset value difference threshold. If the absolute value is greater than the value difference threshold, the EEG signal to be recorded is a normal EEG signal; if the absolute value is less than the value difference threshold, the EEG signal to be recorded is an abnormal EEG signal.

[0087] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 701, a communications interface 702, a memory 703, and a communication bus 704, wherein the processor, communications interface, and memory communicate with each other through the communication bus. The processor can call logical instructions in the memory to execute a method for recording electroencephalogram (EEG) signal data. This method includes: acquiring an EEG signal to be recorded from target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses; statistically analyzing the value of the EEG signal to be recorded to obtain EEG signal characteristics of the target EEG data; classifying and labeling the EEG signal to be recorded according to the EEG signal characteristics, wherein the classification includes: normal EEG signals and abnormal EEG signals; and recording the normal EEG signals and / or abnormal EEG signals.

[0088] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] On the other hand, embodiments of the present invention also provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer is able to execute the method for recording EEG signal data provided in the above-described method embodiments, the method including: acquiring a EEG signal to be recorded from target EEG data, the EEG signal to be recorded being an EEG signal with nerve impulses; statistically analyzing the value of the EEG signal to be recorded to obtain EEG signal characteristics of the target EEG data; classifying and labeling the EEG signal to be recorded according to the EEG signal characteristics, the classification including: normal EEG signal and abnormal EEG signal; and classifying and recording the normal EEG signal and / or abnormal EEG signal.

[0090] In another aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for recording electroencephalogram (EEG) signal data provided in the above embodiments. The method includes: acquiring an EEG signal to be recorded from target EEG data, wherein the EEG signal to be recorded is an EEG signal with nerve impulses; statistically analyzing the values ​​of the EEG signal to be recorded to obtain EEG signal characteristics of the target EEG data; classifying and labeling the EEG signal to be recorded according to the EEG signal characteristics, wherein the classification includes: normal EEG signals and abnormal EEG signals; and classifying and recording the normal EEG signals and / or abnormal EEG signals.

[0091] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0092] The above description is only a partial implementation of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of electroencephalographic signal data recording, characterized by, The method comprises: acquiring a to-be-recorded electroencephalogram signal in target electroencephalogram data, comprising: extracting an electroencephalogram signal with a nerve impulse waveform from a complete electroencephalogram data, and marking it as a to-be-recorded electroencephalogram signal; acquiring electroencephalogram signal features of the target electroencephalogram data; classifying and marking the to-be-recorded electroencephalogram signal according to the electroencephalogram signal features, the classification comprising: normal electroencephalogram signal and abnormal electroencephalogram signal; classifying and recording the normal electroencephalogram signal and / or the abnormal electroencephalogram signal; the electroencephalogram signal features comprise: action potential threshold, waveform envelope feature point, wave peak number, wave trough number, duration and / or power spectral density; the acquiring of the electroencephalogram signal features of the target electroencephalogram data comprises: counting the number of wave peaks and / or wave troughs in the to-be-recorded electroencephalogram signal within a set time period, and acquiring the numerical value of each wave peak and / or wave trough; acquiring average wave peak value and / or average wave trough value according to the number and numerical value; the classifying and marking of the to-be-recorded electroencephalogram signal according to the electroencephalogram signal features comprises: acquiring wave peak value difference and / or wave trough value difference between the wave peak value and / or wave trough value of the to-be-recorded electroencephalogram signal and the average wave peak value and / or average wave trough value; if the absolute value of the wave peak value difference and / or wave trough value difference is outside the threshold range, the to-be-recorded electroencephalogram signal is an abnormal electroencephalogram signal; and if the absolute value of the wave peak value difference and / or wave trough value difference is within the threshold range, the to-be-recorded electroencephalogram signal is a normal electroencephalogram signal; the classifying and recording of the normal electroencephalogram signal and / or the abnormal electroencephalogram signal comprises: creating corresponding record information according to the normal electroencephalogram signal and / or the abnormal electroencephalogram signal to establish an index library, wherein each piece of record information comprises an index segment, a data segment and a signal type; the index segment comprises an index address of the electroencephalogram signal, the data segment comprises the action potential threshold, waveform envelope feature point, wave peak number, wave trough number, duration and / or power spectral density of the electroencephalogram signal, and the signal type comprises normal electroencephalogram signal, abnormal electroencephalogram signal and ambiguous electroencephalogram signal; wherein the ambiguous electroencephalogram signal refers to a signal that cannot be currently determined as an abnormal electroencephalogram signal or a normal electroencephalogram signal.

2. A system for electroencephalographic data recording, characterized by The system is used for executing the method of claim 1, and comprises: a to-be-recorded electroencephalogram signal acquisition module, which is used for acquiring a to-be-recorded electroencephalogram signal in target electroencephalogram data, the to-be-recorded electroencephalogram signal being an electroencephalogram signal with nerve impulses; an electroencephalogram signal feature acquisition module, which is used for counting the numerical value of the to-be-recorded electroencephalogram signal, and acquiring electroencephalogram signal features of the target electroencephalogram data; a classifying and marking module, which is used for classifying and recording normal electroencephalogram signals and / or abnormal electroencephalogram signals, the classification comprising: normal electroencephalogram signal and abnormal electroencephalogram signal; an index address recording module, which is used for recording corresponding index addresses of the normal electroencephalogram signals and / or the abnormal electroencephalogram signals respectively.

3. The system of claim 2, wherein, the electroencephalogram signal feature acquisition module comprises: a quantity and value obtaining unit configured to count a quantity of wave crests and / or wave troughs in the brain electrical signal to be recorded and obtain a value of each wave crest and / or wave trough; an average value obtaining unit configured to obtain an average wave crest value and / or an average wave trough value according to the quantity and value.

4. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method for recording brain electrical signal data according to claim 1 when executing the program.

5. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the method for recording brain electrical signal data according to claim 1 when executed by the processor.

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