Electroencephalogram physical sign detection and evaluation method and system, electronic equipment and storage medium

Abstract

The invention provides an electroencephalogram physical sign detection and evaluation method and system, electronic equipment and a storage medium. The system comprises a periaural flexible myoelectricity sensor, a periorbital flexible myoelectricity sensor and a wearable in-ear sensor system. A cochlea sensor and an auditory meatus flexible myoelectricity sensor of the wearable in-ear sensor system are respectively used for monitoring electroencephalogram information and collecting auditory meatus myoelectricity signals; the periauricular flexible myoelectricity sensor is used for collecting periauricular myoelectricity signals; the periocular flexible electromyographic sensor is used for collecting periocular electromyographic signals; and the wearable in-ear electrophysiological system is used for performing fatigue detection and evaluation according to the electroencephalogram information, the ear canal electromyographic signals, the periear electromyographic signals and the periocular electromyographic signals. According to the method, a human body drowsiness evaluation model is constructed by using sEMG array image information and electroencephalogram information of different muscle groups, evaluation and prediction of fatigue judgment are realized by using the priori of myoelectricity information and adopting a classifier algorithm, and the method plays an important role in monitoring drowsiness and health status of a driver.

Description

Technical Field

[0001] The present invention relates to the technical field of wearable fatigue detection, and in particular to a method, system, electronic device and storage medium for detecting and evaluating electroencephalogram (EEG) signs. Background Art

[0002] Driving fatigue refers to the phenomenon that the driver's physiological and psychological functions are disturbed after long-term continuous driving, and the driving skills are objectively reduced. Drivers with poor or insufficient sleep and long-term driving are prone to fatigue. Driving fatigue will affect the driver's attention, feeling, perception, thinking, judgment, will, decision and movement.

[0003] Neural wearable devices play an important role in monitoring the driver's sleepiness and health status. At present, sleepiness monitoring solutions such as camera-based eye tracking, steering wheel trajectory sensors or electrophysiological recording devices have been widely used in vehicle scenarios, but eye tracking has the problem of being easily blocked by sunglasses and other obstacles, and most electrophysiological recording devices have large electrodes and large size, which are inconvenient to use, limiting their daily application. Summary of the invention

[0004] In order to achieve the above-mentioned purpose and other advantages according to the present invention, the first object of the present invention is to provide a method for detecting and evaluating electroencephalogram signs, comprising the following steps: Process the ear canal electromyographic signals, ear periocular electromyographic signals, eye periocular electromyographic signals, and EEG information to improve signal quality; Extracting the time domain features and frequency domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information; Performing feature-level fusion on the features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information to form a high-dimensional feature vector; Using multiple basic classifiers to analyze the high-dimensional feature vector; The prediction results of the plurality of basic classifiers are used as new input features to train a meta-classifier, and a final judgment is made by integrating the information of all classifiers; The boosting method is used to iteratively train multiple weak classifiers to make them strong classifiers to improve the accuracy of the overall model.

[0005] Furthermore, the step of processing the ear canal electromyographic signal, the ear periocular electromyographic signal, the eye periocular electromyographic signal, and the electroencephalogram information includes: Performing bandpass filtering on the ear canal electromyographic signal, the ear periocular electromyographic signal, the eye periocular electromyographic signal, and the EEG information; Perform multi-scale decomposition on the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the EEG information by using wavelet transform, and reconstruct the signal after removing noise; The reconstructed signal was high-pass filtered to remove the baseline drift; Use low-order polynomial fitting to correct baseline drift to further improve signal quality; All collected signals are then normalized to eliminate differences between individuals.

[0006] Furthermore, the extracting of the time domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information includes: Calculate the mean, standard deviation, RMS value, and peak-to-peak value of the ear canal electromyographic signal, the periauricular electromyographic signal, the periocular electromyographic signal, and the electroencephalogram information.

[0007] Furthermore, extracting the frequency domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information includes: Perform fast Fourier transform on the ear canal electromyographic signal, the ear periauricular electromyographic signal, the eye periauricular electromyographic signal, and the electroencephalogram information, analyze the power spectral density of different frequency components, calculate the average frequency of the ear canal electromyographic signal, the ear periauricular electromyographic signal, the eye periauricular electromyographic signal, and the electroencephalogram information, analyze the changes in frequency components under fatigue state, and then use continuous wavelet transform to analyze the multi-scale characteristics of the signal.

[0008] Furthermore, the method further comprises the steps of: A dimensionality reduction method is used to reduce the dimension of the high-dimensional feature vector to eliminate redundant features.

[0009] Furthermore, support vector machine, random forest, and extreme gradient boosting algorithms are used as basic classifiers.

[0010] The second object of the present invention is to provide an electronic device, comprising: a memory on which a program code is stored; a processor connected to the memory and implementing the above method when the program code is executed by the processor.

[0011] A third object of the present invention is to provide a computer-readable storage medium having program instructions stored thereon, wherein the program instructions implement the above method when executed.

[0012] The fourth object of the present invention is to provide a brain electroencephalogram sign detection and evaluation system for implementing the above method, including a flexible myoelectric sensor around the ear, a flexible myoelectric sensor around the eye, and a wearable in-ear sensor system; wherein, The wearable in-ear sensor system includes a cochlear sensor and an ear canal flexible electromyographic sensor; The cochlear sensor is used to monitor EEG information; The ear canal flexible electromyographic sensor is used to collect ear canal electromyographic signals; The periauricular flexible electromyographic sensor is used to collect periauricular electromyographic signals; The periocular flexible electromyography sensor is used to collect periocular electromyography signals; The wearable in-ear electrophysiological system is used to detect and evaluate fatigue based on the EEG information, the ear canal electromyographic signals, the periauricular electromyographic signals, and the periocular electromyographic signals.

[0013] Furthermore, the cochlear sensor uses an electrochemical and electrophysiological sensor array integrated in the ear, and the electrochemical and electrophysiological sensor array is used to record brain activity and sweat secretion information.

[0014] Furthermore, the cochlear sensor is attached to the earplug to form an integrated sensor assembled in the ear.

[0015] Furthermore, the ear canal flexible electromyographic sensor includes an EEG detection layer and an electrothermal driving layer, wherein the electrothermal driving layer is used to apply Joule heating generated by an external electric field to trigger the shape memory effect and cause expansion into a predetermined spiral shape with a larger radius, and the EEG detection layer is used to detect the ear canal electromyographic signal.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses multimodal information such as sEMG array at the ear side, wearable in-ear sensor system and periocular sEMG array for fatigue detection and assessment for sleepiness monitoring. The wireless dry electrode earplug integrates dry electrodes, wireless electronic devices and offline classification algorithms. The present invention uses a support vector machine (SVM) classification algorithm and finds that the wireless dry electrode earplug is comparable to the existing wet electrode acquisition system (such as in-ear EEG and scalp EEG system) in terms of sleepiness classification accuracy. The application of the new wireless dry electrode earplug will lay the foundation for future covert, wireless and long-term brain monitoring.

[0017] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a monitoring schematic diagram of the EEG sign detection and evaluation system of Example 1; Figure 2 Schematic diagram of the earplug-type cochlear sensor of Example 1; Figure 3 Schematic diagram of the ear canal flexible electromyographic sensor of Example 1; Figure 4 This is a flow chart of the method for detecting and evaluating electroencephalogram signs in Example 2; Figure 5 is a signal processing flow chart of Example 2; Figure 6 This is a schematic diagram of an electronic device according to Embodiment 3; Figure 7 This is a schematic diagram of the storage medium of Example 4. DETAILED DESCRIPTION

[0019] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0020] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0021] The figure numbers in this application are only used to distinguish the various steps in the scheme and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] Example 1 A system for detecting and evaluating electroencephalogram signs, such as Figure 1 As shown, it includes a flexible myoelectric sensor around the ear, a flexible myoelectric sensor around the eye, and a wearable in-ear sensor system; wherein, The wearable in-ear sensor system includes a cochlear sensor and an ear canal flexible electromyographic sensor; The cochlear sensor is used to monitor EEG information; The ear canal flexible electromyographic sensor is used to collect ear canal electromyographic signals; The periauricular flexible electromyographic sensor is used to collect periauricular electromyographic signals; The periocular flexible electromyography sensor is used to collect periocular electromyography signals; The wearable in-ear electrophysiological system is used to detect and evaluate fatigue based on the EEG information, the ear canal electromyographic signals, the periauricular electromyographic signals, and the periocular electromyographic signals.

[0024] Due to the proximity of the ear canal to the central nervous system, wearable in-ear sensor systems can be used to monitor brain states unobtrusively. The wearable in-ear sensor system designs an in-ear integrated electrochemical and electrophysiological sensor array by utilizing the exocrine sweat glands of the ear. The cochlear sensors are placed on a flexible substrate surrounding the user's universal earphones to simultaneously monitor lactate concentration and brain states via electroencephalogram (EEG), electrooculogram (EOG), and electrodermal activity (EDA), such as Figure 2 shown.

[0025] The design of the integrated sensor in the ear includes electrophysiological and electrochemical sensors for recording brain activity and sweat secretion, and integrated electrophysiological (ePhys) and electrochemical (eChem) sensing electrodes in the ear. From bottom to top, the sensor is made of adhesive, TPU, SEBS, PB, stretchable silver, PVA hydrogel, and flexible printed circuit board (fPCB). The flexible sensor is stretched and deformed and attached to the earplug to form an integrated sensor assembled in the ear, namely the cochlear sensor.

[0026] The ear canal flexible electromyographic sensor can adaptively expand and spiral along the ear canal under electrothermal drive to ensure close contact. The components include an EEG detection layer (EEGDL) and an electrothermal drive layer (EAL). Since the shape and size of each person's auditory canal are different, and the internal structure is tortuous, the ear canal flexible electromyographic sensor is introduced into the ear canal in a compressed temporary shape (a smaller spiral form). Then, the shape memory effect is triggered by applying Joule heating generated by an external electric field, causing expansion into a predetermined spiral shape with a larger radius, and the EEG detection layer is used to detect the ear canal electromyographic signal. In order to form a close contact with the inner wall of the ear canal, the radius of the predetermined spiral is designed to be larger than the radius of the ear canal. During the shape recovery process, it is hindered by the ear canal wall to conform to the shape of the auditory canal, effectively solving the differences and complexities of users' ear canals, such as Figure 3 shown.

[0027] In order to solve the problem of low recognition rate of fatigue discrimination by a single classifier, the present invention proposes a multi-weak classifier signal information fusion classification method based on a parallel multi-source information fusion algorithm. Based on the parallel multi-source information fusion algorithm structure, a multi-weak classifier signal information fusion classification system is designed according to the number of weak classifiers and input data, and the weak classifier results are fused using a multi-mode information fusion method. For a detailed description of the EEG sign detection and evaluation method corresponding to the EEG sign detection and evaluation system, please refer to the corresponding description in the following method embodiment, which will not be repeated here.

[0028] This embodiment uses multimodal information such as sEMG array at the ear side, wearable in-ear sensor system and periocular sEMG array for fatigue detection and assessment for sleepiness monitoring. The wireless dry electrode earplug integrates dry electrodes, wireless electronic devices and offline classification algorithms. This embodiment uses a support vector machine (SVM) classification algorithm and finds that the wireless dry electrode earplug is comparable to the existing wet electrode acquisition system (such as in-ear EEG and scalp EEG system) in terms of sleepiness classification accuracy. The application of the new wireless dry electrode earplug will lay the foundation for future covert, wireless and long-term brain monitoring.

[0029] Example 2 The EEG sign detection and evaluation method corresponding to the EEG sign detection and evaluation system provided in Example 1 can be referred to the corresponding description in the above system embodiment for detailed description of the system, which will not be repeated here. Figure 4 As shown, the method comprises the following steps: S1, processing ear canal electromyographic signals, ear periocular electromyographic signals, eye periocular electromyographic signals, and brain wave information to improve signal quality; like Figure 5 As shown, the steps of processing ear canal electromyographic signals, ear periocular electromyographic signals, eye periocular electromyographic signals, and brain wave information include: S11, performing bandpass filtering on the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the EEG information to filter out low and high frequency noise, etc.; S12, using wavelet transform to perform multi-scale decomposition on the ear canal electromyographic signal, the ear periocular electromyographic signal, the eye periocular electromyographic signal, and the electroencephalogram information, and reconstruct the signal after removing noise; for example, using Daubechies wavelet transform to perform multi-scale decomposition on the ear canal electromyographic signal, the ear periocular electromyographic signal, the eye periocular electromyographic signal, and the electroencephalogram information, and reconstruct the signal after removing noise; S13, high-pass filtering the reconstructed signal to remove baseline drift and ensure signal stability; S14, use low-order polynomial fitting and correction of baseline drift to further improve signal quality; S15, normalizing all collected signals to eliminate differences between individuals. For example, performing Z-score normalization on all collected signals to eliminate differences between individuals and ensure consistency of subsequent analysis.

[0030] S2, extracting the time domain features and frequency domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the EEG information; In some embodiments, extracting the time domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the EEG information includes: Calculate the mean, standard deviation, RMS value, peak-to-peak value and other parameters of the ear canal electromyographic signal, the ear periocular electromyographic signal, the eye periocular electromyographic signal and the EEG information.

[0031] In some embodiments, extracting the frequency domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the EEG information includes: The ear canal EMG signals, the ear periauricular EMG signals, the eye periauricular EMG signals, and the EEG information are subjected to fast Fourier transform, the power spectral density (PSD) of different frequency components is analyzed, and the average frequency of the ear canal EMG signals, the ear periauricular EMG signals, the eye periauricular EMG signals, and the EEG information is calculated, the change of frequency components under fatigue state is analyzed, and then continuous wavelet transform (CWT) is used to analyze the multi-scale characteristics of the signal.

[0032] S3, performing feature-level fusion on the features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information to form a high-dimensional feature vector; In some embodiments, the steps include: The high-dimensional feature vector is reduced in dimension using a dimensionality reduction method to eliminate redundant features. For example, PCA or LDA is used to reduce in dimension the high-dimensional feature vector to eliminate redundant features.

[0033] S4. Use multiple basic classifiers to analyze the high-dimensional feature vector; for example, use algorithms such as support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) as basic classifiers.

[0034] S5, using the prediction results of the multiple basic classifiers as new input features, training a meta-classifier, and making a final judgment by integrating the information of all classifiers; S6. Use the boosting method to iteratively train multiple weak classifiers to make them strong classifiers, thereby improving the accuracy of the overall model. For example, use the boosting method to iteratively train multiple weak classifiers to make them strong classifiers, thereby improving the accuracy of the overall model.

[0035] This embodiment uses sEMG array image information of different muscle groups and EEG information to construct a human sleepiness assessment model, utilizes the a priori nature of electromyography information, and adopts a classifier algorithm to achieve fatigue discrimination evaluation and prediction, which plays an important role in monitoring the driver's sleepiness and health status.

[0036] The invention is based on a parallel multi-source information fusion algorithm structure, designs a multi-weak classifier signal information fusion classification system according to the number of weak classifiers and the difference in input data, and uses a multi-mode information fusion method to fuse the weak classifier results.

[0037] Example 3 An electronic device, such as Figure 6 As shown, it includes: a memory on which program codes are stored; a processor connected to the memory, and when the program codes are executed by the processor, a method for detecting and evaluating EEG signs is implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0038] Example 4 A computer readable storage medium such as Figure 7 As shown, program instructions are stored thereon, and when the program instructions are executed, a method for detecting and evaluating EEG signs is implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, and no further description is given here.

[0039] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0040] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0041] The apparatus, electronic device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, electronic device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, electronic device and non-volatile computer storage medium will not be repeated here.

[0042] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software units for implementing the method and structures within the hardware component.

[0043] The systems, devices or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions in various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or more software and / or hardware.

[0044] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0045] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0048] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0049] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0050] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0051] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for detecting and evaluating electroencephalogram signs, characterized in that: The following steps are involved: Process the ear canal electromyographic signals, ear periocular electromyographic signals, eye periocular electromyographic signals, and EEG information to improve signal quality; Extracting the time domain features and frequency domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information; Performing feature-level fusion on the features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information to form a high-dimensional feature vector; Using multiple basic classifiers to analyze the high-dimensional feature vector; The prediction results of the plurality of basic classifiers are used as new input features to train a meta-classifier, and a final judgment is made by integrating the information of all classifiers; The boosting method is used to iteratively train multiple weak classifiers to make them strong classifiers to improve the accuracy of the overall model.

2. A method for detecting and evaluating electroencephalogram signs as claimed in claim 1, characterized in that: The steps of processing the ear canal electromyographic signal, the ear periocular electromyographic signal, the eye periocular electromyographic signal, and the brain wave information include: Performing bandpass filtering on the ear canal electromyographic signal, the ear periocular electromyographic signal, the eye periocular electromyographic signal, and the EEG information; Perform multi-scale decomposition on the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the EEG information by using wavelet transform, and reconstruct the signal after removing noise; The reconstructed signal was high-pass filtered to remove the baseline drift; Use low-order polynomial fitting to correct baseline drift to further improve signal quality; All collected signals are then normalized to eliminate differences between individuals.

3. A method for detecting and evaluating electroencephalogram signs as claimed in claim 1, characterized in that: The extracting of the time domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalography information includes: Calculate the mean, standard deviation, RMS value, and peak-to-peak value of the ear canal electromyographic signal, the periauricular electromyographic signal, the periocular electromyographic signal, and the electroencephalogram information.

4. A method for detecting and evaluating electroencephalogram signs as claimed in claim 1, characterized in that: Extracting the frequency domain features of the ear canal electromyography signal, the ear periocular electromyography signal, the eye periocular electromyography signal, and the electroencephalogram information includes: Perform fast Fourier transform on the ear canal electromyographic signal, the ear periauricular electromyographic signal, the eye periauricular electromyographic signal, and the electroencephalogram information, analyze the power spectral density of different frequency components, calculate the average frequency of the ear canal electromyographic signal, the ear periauricular electromyographic signal, the eye periauricular electromyographic signal, and the electroencephalogram information, analyze the changes in frequency components under fatigue state, and then use continuous wavelet transform to analyze the multi-scale characteristics of the signal.

5. The method for detecting and evaluating electroencephalogram signs according to claim 1, characterized in that: Also includes the steps: A dimensionality reduction method is used to reduce the dimension of the high-dimensional feature vector to eliminate redundant features.

6. A method for detecting and evaluating electroencephalogram signs as claimed in claim 1, characterized in that: Support vector machine, random forest and extreme gradient boosting algorithms are used as basic classifiers.

7. An electronic device, characterized in that: include: A memory having program code stored therein; A processor connected to the memory, and when the program code is executed by the processor, the method according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium, characterized in that: Program instructions are stored thereon, and when the program instructions are executed, the method according to any one of claims 1 to 6 is implemented.

9. A system for detecting and evaluating electroencephalogram signs, used to implement the method according to any one of claims 1 to 6, characterized in that: Including periauricular flexible electromyographic sensors, periocular flexible electromyographic sensors, and wearable in-ear sensor systems; among them, The wearable in-ear sensor system includes a cochlear sensor and an ear canal flexible electromyographic sensor; The cochlear sensor is used to monitor EEG information; The ear canal flexible electromyographic sensor is used to collect ear canal electromyographic signals; The periauricular flexible electromyographic sensor is used to collect periauricular electromyographic signals; The periocular flexible electromyography sensor is used to collect periocular electromyography signals; The wearable in-ear electrophysiological system is used to detect and evaluate fatigue based on the EEG information, the ear canal electromyographic signals, the periauricular electromyographic signals, and the periocular electromyographic signals.

10. The electroencephalogram (EEG) sign detection and evaluation system according to claim 9, characterized in that: The cochlear sensor uses an electrochemical and electrophysiological sensor array integrated in the ear, which is used to record brain activity and sweat secretion information.

11. The electroencephalogram (EEG) sign detection and evaluation system according to claim 10, characterized in that: The cochlear sensor is attached to the earplug to form an integrated sensor assembled in the ear.

12. The electroencephalogram (EEG) sign detection and evaluation system according to claim 9, characterized in that: The ear canal flexible electromyographic sensor includes an EEG detection layer and an electrothermal drive layer. The electrothermal drive layer is used to apply Joule heating generated by an external electric field to trigger the shape memory effect and cause expansion into a predetermined spiral shape with a larger radius. The EEG detection layer is used to detect ear canal electromyographic signals.

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