Brain electrical signal identification method, system, electronic device and storage medium

Abstract

The present invention relates to the field of biometric technology, and specifically discloses a method, system, electronic device, and storage medium for identifying an individual using an electroencephalogram (EEG) signal, comprising: obtaining multiple facial images and original EEG signals of the target user when the target user views a target induced image; determining the target user's eye movement time period based on the multiple facial images of the target user, and deleting the EEG signals corresponding to the eye movement time period from the original EEG signals to obtain an optimized EEG signal and preprocessing the signal to obtain a target EEG signal; performing multimodal encoding on the coding information of the target induced image and the coding information of the target EEG signal to obtain the target user's multimodal coding information and inputting the information into a trained multimodal large model to obtain an identification result. The present invention can effectively solve the data discrepancy problem caused by different induced images, and can efficiently achieve identity identification and verification through the integrated process design of visual induction, video detection, and EEG signal acquisition.

Description

Technical Field

[0001] The present invention relates to the field of biometric identification technology, and in particular to a method, system, electronic device and storage medium for identifying an individual using an electroencephalogram (EEG) signal. Background Art

[0002] With the rapid development of new-generation artificial intelligence technologies, exemplified by deep learning, the research and application of biometrics has reached new heights. Technologies such as facial recognition, fingerprint recognition, finger vein recognition, and iris recognition have been widely adopted in finance, public security, and access control. Simultaneously, academia and industry are constantly exploring new biometric identification methods, such as voiceprint recognition, gait recognition, and DNA-based biometrics. Furthermore, identity recognition based on EEG signals has become a new research hotspot in recent years.

[0003] Since the 1980s, EEG-based biometric identification has undergone decades of development and has made certain phased progress. Currently, many scholars at home and abroad have conducted research on EEG-based identity recognition. With the development of deep learning technology in recent years, many scholars have also begun to try to combine deep learning technology with EEG-based identity recognition research. In 2017, Rig Das et al. tried to use CNN to extract and identify features from VEP data, achieving an accuracy rate of 97% on a data set of 40 people. In 2019, et al. used a CNN as a classifier, using an EEG dataset from the RSVP task, achieving 99.3% accuracy on a test set of 10 subjects. JXchen et al. proposed a GSLT-CNN network architecture, ultimately achieving 96% accuracy on a dataset of 157 subjects using raw, unprocessed data. Mota et al. studied the cross-task integration of biometrics and EEG signals, achieving an equal error rate of 0.1%.

[0004] By investigating the research results of EEG identity recognition at home and abroad, we can find that EEG signals have unique advantages, but also have obvious disadvantages. At present, they are still in the initial stage and are still a long way from being practical. Specifically, they are manifested in several aspects: ① EEG signals are relatively weak and are more susceptible to interference from eye signals, electrocardiogram signals, etc.; ② There is currently no unified induced paradigm for EEG signal identity recognition tasks, and the evaluation data sets selected by researchers are also basically very different, and the research results are not comparable; ③ EEG signal collection is difficult, resulting in a small amount of data. For scenarios such as deep learning that require a large amount of data for training, overfitting is prone to occur.

[0005] The main EEG signal dataset evoked paradigms currently used include resting-state, visual stimulation-evoked potentials, event-related potentials, and motor imagery. Data acquisition based on the resting-state evoked paradigm is relatively easy and does not require active cooperation from the subject, but there is the problem of determining the starting position of the data. The visual stimulation-evoked potential method is currently more common. Because EEG signals are very weak, with amplitudes at the μV level, preprocessing of the collected data is required regardless of the method used. Among these, artifact removal remains the main difficulty in EEG signal processing. Studies have shown that among EEG signal artifacts, eye contact signals are one of the most important EEG signal interference factors.

[0006] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a method, system, electronic device and storage medium for EEG signal identification.

[0008] In a first aspect, the present invention provides a method for identifying an individual using an EEG signal. The technical solution of the method is as follows:

[0009] When a target user views a target induced image, obtaining multiple facial images and original EEG signals of the target user;

[0010] Determining an eye movement time period of the target user based on multiple facial images of the target user, and deleting the EEG signal corresponding to the eye movement time period from the original EEG signal to obtain an optimized EEG signal and preprocessing the optimized EEG signal to obtain a target EEG signal;

[0011] The coding information of the target induced image and the coding information of the target EEG signal are multimodally encoded to obtain the multimodal coding information of the target user and input it into the trained multimodal large model to obtain the identity recognition result of the target user.

[0012] The beneficial effects of the EEG signal identity recognition method of the present invention are as follows:

[0013] The method of the present invention can effectively solve the problem of data differences caused by different induced images. Through the integrated process design of visual induction, video detection and EEG signal acquisition, it can efficiently realize identity recognition and verification, has high practical value, and explores a new path for EEG signal identity recognition.

[0014] Based on the above solution, the EEG signal identity recognition method of the present invention can be further improved as follows.

[0015] In an optional manner, the step of determining the eye movement time period of the target user based on the multiple facial images of the target user includes:

[0016] An eye movement time period representing changes in eye features of the target user is determined by using a feature point algorithm and based on changes in eye feature points in a plurality of facial images of the target user.

[0017] In an optional manner, the preprocessing method is: noise removal.

[0018] In an optional manner, the training process of the multimodal large model is:

[0019] The target EEG signals corresponding to multiple subjects when viewing different induced images are obtained, and multiple training data are generated according to the subject ID, induced image and target EEG signal as one training data. The multiple training data are input into the multimodal large model for iterative training to obtain a trained multimodal large model.

[0020] In an optional manner, the multimodal large model is a multimodal network based on a self-attention mechanism.

[0021] In a second aspect, the present invention provides an EEG signal identity recognition system, the technical solution of which is as follows:

[0022] Includes: acquisition module, processing module and recognition module;

[0023] The acquisition module is used to: when the target user views the target induced image, acquire multiple facial images and original EEG signals of the target user;

[0024] The processing module is configured to: determine the target user's eye movement time period based on the multiple facial images of the target user, and delete the EEG signal corresponding to the eye movement time period from the original EEG signal to obtain an optimized EEG signal and perform preprocessing to obtain a target EEG signal;

[0025] The recognition module is used to: perform multimodal encoding on the coding information of the target induced image and the coding information of the target EEG signal to obtain the multimodal coding information of the target user and input it into the trained multimodal large model to obtain the identity recognition result of the target user.

[0026] The beneficial effects of the EEG signal identity recognition system of the present invention are as follows:

[0027] The system of the present invention can effectively solve the problem of data differences caused by different induced images. Through the integrated process design of visual induction, video detection and EEG signal acquisition, it can efficiently realize identity recognition and verification, has high practical value, and explores a new path for EEG signal identity recognition.

[0028] Based on the above solution, the EEG signal identity recognition system of the present invention can be further improved as follows.

[0029] In an optional manner, the step of determining the target user's eye movement time period based on the multiple facial images of the target user in the processing module includes:

[0030] An eye movement time period representing changes in eye features of the target user is determined by using a feature point algorithm and based on changes in eye feature points in a plurality of facial images of the target user.

[0031] In an optional manner, the preprocessing method is: noise removal.

[0032] In a third aspect, the technical solution of an electronic device of the present invention is as follows:

[0033] The invention comprises a memory, a processor and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the method for identifying an EEG signal are realized.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0035] The computer-readable storage medium stores instructions. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the EEG signal identity recognition method of the present invention.

[0036] 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, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the drawings. In the drawings:

[0038] Figure 1 1 is a flow chart of an embodiment of a method for identifying an individual using an EEG signal;

[0039] Figure 2 This is a schematic diagram of the camera installation;

[0040] Figure 3 Schematic diagram of facial feature points;

[0041] Figure 4 Schematic diagram of eye movement artifact removal;

[0042] Figure 5 Schematic diagram of a multimodal network based on the self-attention mechanism;

[0043] Figure 6 This is a schematic structural diagram of an embodiment of an EEG signal identity recognition system of the present invention;

[0044] Figure 7 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0046] Figure 1 The flowchart of an embodiment of the method for identifying an EEG signal provided by the present invention is shown. The method for identifying an EEG signal can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the method for identifying an EEG signal by calling computer-readable instructions stored in a memory through a processor. Figure 1 As shown, the following steps are included:

[0047] S1. When a target user views a target induced image, multiple facial images and original EEG signals of the target user are obtained.

[0048] The target user is the user whose identity needs to be identified. The target induced image is a randomly generated image that is output and displayed on the display screen.

[0049] The camera is used to collect multiple facial images of the target user, and the multiple facial images are multiple consecutive facial images (continuous frame images) when the target user views the target induced image. Figure 2 As shown, the camera is set above the display screen to capture the facial features and eye movement characteristics of the target user. The raw EEG signal is collected by EEG signal collection electrodes or an EEG cap containing EEG signal collection electrodes.

[0050] It should be noted that in this embodiment, a high-definition camera that can collect the user's facial features and eye movement features is added above the display screen when collecting EEG signals. When the user watches different types of visually induced sample pictures or videos displayed on the screen, the detailed features of their eyes and face are also recorded synchronously.

[0051] S2. Based on the multiple facial images of the target user, determine the eye movement time period of the target user, and delete the EEG signal corresponding to the eye movement time period in the original EEG signal to obtain an optimized EEG signal and preprocess it to obtain a target EEG signal.

[0052] In S2, a feature point algorithm is used to determine an eye movement time period representing changes in the eye features of the target user according to changes in the eye feature points in the plurality of facial images of the target user.

[0053] Among them, Figure 3 As shown, a face and eye feature point algorithm based on deep learning is used to fit the eye-related feature points of the subject, and the eye movement feature patterns (changes in eye feature points) are recorded in real time to determine the eye movement time period that characterizes the changes in eye features.

[0054] Among them, in the original EEG signal obtained, the time slices with obvious eye movement characteristics (eye movement time period) are deleted to obtain the EEG signal without eye movement artifact interference, that is, the EEG signal is optimized. Figure 4 The figure shows a typical EEG signal (raw EEG signal) containing eye movement artifacts. The area within the frame shows the severe interference caused by eye movement artifacts. By detecting the user's eye feature points, we can identify the time slices (eye movement time periods) with eye movement artifacts. In the application process, simply remove the signal in the framed area to obtain an EEG signal without obvious eye movement artifacts, that is, an optimized EEG signal.

[0055] The default preprocessing method is noise removal, which can be adjusted according to actual conditions and is not limited here. The target EEG signal is the EEG signal after noise removal of the optimized EEG signal.

[0056] S3. Perform multimodal encoding on the coding information of the target induced image and the coding information of the target EEG signal to obtain the multimodal coding information of the target user and input it into the trained multimodal large model to obtain the identity recognition result of the target user.

[0057] In S3, the multimodal large model is a multimodal network based on the self-attention mechanism. The training process of the multimodal large model is:

[0058] The target EEG signals corresponding to multiple subjects when viewing different induced images are obtained, and multiple training data are generated according to the subject ID, induced image and target EEG signal as one training data. The multiple training data are input into the multimodal large model for iterative training to obtain a trained multimodal large model.

[0059] Each training data is: {experimenter ID, evoked image, target EEG signal}. The advantage of a large multimodal model is that it can fully utilize the information of different media data to extract richer and more comprehensive features, thereby improving the performance and generalization ability of the model. In addition, the large multimodal model can further enhance the semantic understanding and expression ability of the model by learning the association between different modalities. Figure 5 As shown in the figure, the induced image and the target EEG signal preprocessed by the subject based on the EEG signal acquisition results induced by the image are separately encoded (embedding), and then multimodally encoded (embedding) and uniformly input into a multimodal network based on the self-attention mechanism for processing. After passing through the multi-layer network, a feature sequence is output, and then a softmax classifier is connected to the feature sequence to obtain the corresponding identity recognition result.

[0060] It should be noted that for the same subject, all factors that cause different induced images to produce different induced EEG signals are taken into consideration, and the induced image is equivalent to a "reference"; for different subjects, for the same induced image, the results of induced image encoding are consistent, and only the induced EEG signal data are different, so it can be used to distinguish different subjects.

[0061] The technical solution of this embodiment can effectively solve the problem of data differences caused by different induced images. Through the integrated process design of visual induction, video detection and EEG signal acquisition, it can efficiently realize identity recognition and verification, has high practical value, and explores a new path for EEG signal identity recognition.

[0062] Figure 6 FIG. 2 shows a schematic diagram of an embodiment of an EEG signal identification system 200 provided by the present invention. Figure 6 As shown, the system 200 includes: an acquisition module 210, a processing module 220 and an identification module 230;

[0063] The acquisition module 210 is used to: when the target user views the target induced image, acquire multiple facial images and original EEG signals of the target user;

[0064] The processing module 220 is configured to: determine the target user's eye movement time period based on the multiple facial images of the target user, and delete the EEG signal corresponding to the eye movement time period from the original EEG signal to obtain an optimized EEG signal and perform preprocessing to obtain a target EEG signal;

[0065] The recognition module 230 is used to: perform multimodal encoding on the coding information of the target induced image and the coding information of the target EEG signal to obtain the multimodal coding information of the target user and input it into the trained multimodal large model to obtain the identity recognition result of the target user.

[0066] In an optional manner, the step of determining the target user's eye movement time period based on the multiple facial images of the target user in the processing module 220 includes:

[0067] An eye movement time period representing changes in eye features of the target user is determined by using a feature point algorithm and based on changes in eye feature points in a plurality of facial images of the target user.

[0068] In an optional manner, the preprocessing method is: noise removal.

[0069] In an optional manner, the training process of the multimodal large model is:

[0070] The target EEG signals corresponding to multiple subjects when viewing different induced images are obtained, and multiple training data are generated according to the subject ID, induced image and target EEG signal as one training data. The multiple training data are input into the multimodal large model for iterative training to obtain a trained multimodal large model.

[0071] In an optional manner, the multimodal large model is a multimodal network based on a self-attention mechanism.

[0072] The technical solution of this embodiment can effectively solve the problem of data differences caused by different induced images. Through the integrated process design of visual induction, video detection and EEG signal acquisition, it can efficiently realize identity recognition and verification, has high practical value, and explores a new path for EEG signal identity recognition.

[0073] The above parameters and steps for each module to implement corresponding functions in the EEG signal identification system 200 of this embodiment can refer to the parameters and steps in the embodiment of the EEG signal identification method above, and will not be repeated here.

[0074] like Figure 7As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330. The at least one computer program 330 is loaded and executed by the processor 320 to enable the electronic device 300 to implement any of the above-mentioned methods for identifying an EEG signal identity. Specifically:

[0075] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320 to enable the electronic device 300 to implement any of the EEG signal identification methods provided in the above embodiments. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The electronic device 300 may also include other components for implementing device functions, which will not be described in detail here.

[0076] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-mentioned EEG signal identity recognition methods.

[0077] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0078] In an exemplary embodiment, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the above-described methods for identifying an EEG signal.

[0079] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0080] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0081] Any combination of one or more computer-readable media can be used. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer 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 thereof. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0082] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for identifying an individual using an EEG signal, characterized in that: include: When a target user views a target induced image, obtaining multiple facial images and original EEG signals of the target user; Using a deep learning-based face and eye feature point algorithm, and based on changes in eye feature points in multiple facial images of the target user, determining an eye movement time period that characterizes changes in the target user's eye features, and deleting the EEG signal corresponding to the eye movement time period from the original EEG signal to obtain an optimized EEG signal without eye movement artifact interference, and performing preprocessing to obtain a target EEG signal; The coding information of the target induced image and the coding information of the target EEG signal are multimodally encoded to obtain the multimodal coding information of the target user and input it into the trained multimodal large model to obtain the identity recognition result of the target user.

2. The method for identifying an individual using an EEG signal according to claim 1, wherein: The preprocessing method is: noise removal.

3. The method for identifying an individual using an EEG signal according to claim 1, wherein: The training process of the multimodal large model is as follows: The target EEG signals corresponding to multiple subjects when viewing different induced images are obtained, and multiple training data are generated according to the subject ID, induced image and target EEG signal as one training data. The multiple training data are input into the multimodal large model for iterative training to obtain a trained multimodal large model.

4. The method for identifying an individual using an EEG signal according to any one of claims 1 to 3, wherein: The multimodal large model is a multimodal network based on the self-attention mechanism.

5. An EEG signal identity recognition system, characterized in that: include: Acquisition module, processing module and recognition module; The acquisition module is used to: when the target user views the target induced image, acquire multiple facial images and original EEG signals of the target user; The processing module is configured to: utilize a face and eye feature point algorithm based on deep learning, and determine an eye movement time period characterizing changes in the eye features of the target user based on changes in the eye feature points in multiple facial images of the target user, and delete the EEG signal corresponding to the eye movement time period from the original EEG signal to obtain an optimized EEG signal without interference from eye movement artifacts, and perform preprocessing to obtain a target EEG signal; The recognition module is used to: perform multimodal encoding on the coding information of the target induced image and the coding information of the target EEG signal to obtain the multimodal coding information of the target user and input it into the trained multimodal large model to obtain the identity recognition result of the target user.

6. The EEG signal identification system according to claim 5, characterized in that: The preprocessing method is: noise removal.

7. An electronic device, characterized in that: The electronic device includes a processor, which is coupled to a memory. The memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the EEG signal identity recognition method as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor so that the computer-readable storage medium implements the EEG signal identity recognition method according to any one of claims 1 to 4.

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