Nonlinear energy operator feature extraction method suitable for brain-computer steady-state signal

Abstract

The invention relates to a nonlinear energy operator feature extraction method and device suitable for brain-computer steady-state signals, electronic equipment and a storage medium. The method comprises the following steps: collecting multi-channel electroencephalogram data, and carrying out data preprocessing on the multi-channel electroencephalogram data; calculating a product difference value between the multi-channel electroencephalogram data subjected to data preprocessing and adjacent data points, and generating a nonlinear capability feature; and based on a TRCA algorithm and according to the nonlinear capability characteristics, generating an integrated projection matrix by calculating projection vectors, and realizing classification and identification of the nonlinear capability characteristics based on the integrated projection matrix. Through a universal feature extraction strategy, the method can be further combined with a current SSVEP feature extraction or classification algorithm, and improves the performance of a BCI system.

Description

Background Art

[0002] Brain-Computer Interface (BCI) is a system that directly converts central nervous system activity into artificial output. It can replace, repair, enhance, supplement or improve the normal output of the central nervous system, thereby improving the interaction between the central nervous system and the internal and external environment. The core technology of brain-computer interface is to explore brain information and decode user intentions. The main indicators for evaluating the performance of BCI systems are recognition accuracy and information transfer rate (ITR). Conventional brain-computer interface systems include five consecutive stages: signal acquisition, preprocessing, feature extraction, and classification and recognition. Signal acquisition refers to the use of neuroimaging technology to obtain brain activity information, which is the basis of BCI technology. The main purpose of preprocessing is to reduce the impact of noise and improve the quality of EEG signals, including filtering, downsampling, data segmentation, baseline correction, artifact removal and other steps. Feature extraction is mainly to extract the EEG features of interest from the preprocessed data. The common method is to analyze the characteristics of the signal in the time domain, frequency domain, spatial domain, etc., map the signal to a space with discriminant function, and provide effective feature information for subsequent classification and recognition. The classification and recognition stage is to apply algorithms such as machine learning or deep learning, establish a corresponding mathematical model, identify the extracted features, and output corresponding control instructions.

[0003] Steady-state visual evoked potential (SSVEP) is a neural response induced in the brain when the eyes focus on visual stimuli that flicker at a constant frequency (not less than 6Hz). It is mainly distributed in the primary visual cortex of the brain, that is, the occipital region of the brain. Its characteristic is that effective response amplitudes can be observed at the fundamental frequency and each multiple of the flicker frequency. The BCI system based on SSVEP has the advantages of stable induced characteristics and high communication rate. The extraction of SSVEP features is the key to the SSVEP-BCI system. At present, methods such as power spectral density, filter bank, canonical correlation analysis, and task-related analysis have been developed to extract SSVEP features from the frequency domain, time domain, spatial domain or time-space joint dimensions. However, the amount of feature information that can be extracted from the above dimensions is limited, resulting in the performance of the SSVEP-BCI system still needs to be improved. Therefore, it is urgent to propose a new feature extraction method.

[0004] Therefore, one or more methods are needed to solve the above problems.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of the present disclosure is to provide a nonlinear energy operator feature extraction method, device, electronic device and computer-readable storage medium suitable for brain-computer steady-state signals, thereby overcoming one or more problems caused by the limitations and defects of related technologies at least to a certain extent.

[0007] According to one aspect of the present disclosure, a nonlinear energy operator feature extraction method applicable to brain-computer steady-state signals is provided, comprising:

[0008] Collecting multi-channel EEG data and performing data preprocessing on the multi-channel EEG data;

[0009] For the multi-channel EEG data after data preprocessing, the product difference with the adjacent data points is calculated to generate nonlinear ability characteristics;

[0010] Based on the TRCA algorithm, according to the nonlinear capability characteristics, an integrated projection matrix is ​​generated by calculating projection vectors, and classification and recognition of the nonlinear capability characteristics are achieved based on the integrated projection matrix.

[0011] In an exemplary embodiment of the present disclosure, the method further includes:

[0012] The multi-channel EEG data is subjected to data preprocessing including filtering, downsampling, data segmentation, baseline correction, artifact removal, re-referencing, and elimination of bad leads or bad segments.

[0013] In an exemplary embodiment of the present disclosure, the method further includes:

[0014] Based on the fact that the product difference of adjacent data points of evoked responses in multi-channel EEG data is proportional to the amplitude and frequency of the evoked responses, the product difference with adjacent data points is calculated for the multi-channel EEG data after data preprocessing to generate nonlinear ability characteristics.

[0015] In an exemplary embodiment of the present disclosure, the TRCA algorithm in the method further comprises:

[0016] Calculating the sum of the covariances between trials of the multi-channel EEG data according to the nonlinear ability characteristics;

[0017] Based on the preset constraints, the objective function is converted into an optimization problem, and the analytical solution of the objective function is calculated based on the Lagrange multiplier method;

[0018] Based on the analytical solution of the objective function, an integrated projection matrix is ​​generated.

[0019] In an exemplary embodiment of the present disclosure, the method further includes:

[0020] The analytical solution of the objective function is the eigenvector corresponding to the maximum eigenvalue after eigendecomposing the nonlinear capability feature.

[0021] In an exemplary embodiment of the present disclosure, the method further includes:

[0022] Based on the analytical solution of the objective function, the projection vectors of all stimulation frequencies are concatenated column-wise to obtain an integrated projection matrix.

[0023] In an exemplary embodiment of the present disclosure, the method further includes:

[0024] The correlation coefficient between the test sample and the EEG template is calculated, and the category of the test sample is predicted based on the preset discrimination criteria.

[0025] In one aspect of the present disclosure, a nonlinear energy operator feature extraction device suitable for brain-computer steady-state signals is provided, comprising:

[0026] A data preprocessing module, used for collecting multi-channel EEG data and performing data preprocessing on the multi-channel EEG data;

[0027] The feature calculation module is used to calculate the product difference between the multi-channel EEG data after data preprocessing and the adjacent data points to generate nonlinear ability features;

[0028] The classification and recognition module is used to generate an integrated projection matrix by calculating the projection vector based on the TRCA algorithm and the nonlinear capability characteristics, and to realize the classification and recognition of the nonlinear capability characteristics based on the integrated projection matrix.

[0029] In one aspect of the present disclosure, there is provided an electronic device, comprising:

[0030] Processor; and

[0031] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of the above items.

[0032] In one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the above items is implemented.

[0033] In an exemplary embodiment of the present disclosure, a nonlinear energy operator feature extraction method suitable for brain-computer steady-state signals is disclosed, wherein the method includes: collecting multi-channel EEG data and performing data preprocessing on the multi-channel EEG data; calculating the product difference between the multi-channel EEG data after data preprocessing and the adjacent data points to generate nonlinear ability features; based on the TRCA algorithm, according to the nonlinear ability features, generating an integrated projection matrix by calculating the projection vector, and realizing classification and recognition of nonlinear ability features based on the integrated projection matrix. The present disclosure can further combine with the current SSVEP feature extraction or classification algorithm through a general feature extraction strategy to improve the performance of the BCI system.

[0034] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and other features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0036] Figure 1 A flowchart of a nonlinear energy operator feature extraction method applicable to brain-computer steady-state signals according to an exemplary embodiment of the present disclosure is shown;

[0037] Figure 2 Another flow chart of a nonlinear energy operator feature extraction method applicable to brain-computer steady-state signals according to an exemplary embodiment of the present disclosure is shown;

[0038] Figure 3 A structural block diagram of a nonlinear energy operator feature extraction device suitable for brain-computer steady-state signals according to an exemplary embodiment of the present disclosure is shown;

[0039] Figure 4 A block diagram schematically shows an electronic device according to an exemplary embodiment of the present disclosure;

[0040] Figure 5 A schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0041] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0042] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. may be adopted. In other cases, known structures, methods, devices, implementations, materials or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0043] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or these functional entities or parts of functional entities may be implemented in one or more software hardened modules, or these functional entities may be implemented in different networks and / or processor devices and / or microcontroller devices.

[0044] In this exemplary embodiment, a nonlinear energy operator feature extraction method suitable for brain-computer steady-state signals is first provided; Figure 1 As shown in , the nonlinear energy operator feature extraction method suitable for brain-computer steady-state signals may include the following steps:

[0045] Step S110, collecting multi-channel EEG data and performing data preprocessing on the multi-channel EEG data;

[0046] Step S120, calculating the product difference between the multi-channel EEG data after data preprocessing and the adjacent data points to generate nonlinear ability characteristics;

[0047] Step S130, based on the TRCA algorithm, according to the nonlinear capability characteristics, an integrated projection matrix is ​​generated by calculating projection vectors, and classification and recognition of the nonlinear capability characteristics are implemented based on the integrated projection matrix.

[0048] In an exemplary embodiment of the present disclosure, a nonlinear energy operator feature extraction method suitable for brain-computer steady-state signals is disclosed, wherein the method includes: collecting multi-channel EEG data and performing data preprocessing on the multi-channel EEG data; calculating the product difference between the multi-channel EEG data after data preprocessing and the adjacent data points to generate nonlinear ability features; based on the TRCA algorithm, according to the nonlinear ability features, generating an integrated projection matrix by calculating the projection vector, and realizing classification and recognition of nonlinear ability features based on the integrated projection matrix. The present disclosure can further combine with the current SSVEP feature extraction or classification algorithm through a general feature extraction strategy to improve the performance of the BCI system.

[0049] Next, a nonlinear energy operator feature extraction method suitable for brain-computer steady-state signals in this example embodiment will be further described.

[0050] Embodiment 1:

[0051] In the embodiment of this example, the current SSVEP-BCI method has a limited amount of extractable feature information, which leads to the problem that the performance needs to be further improved. Starting from the nonlinear energy dimension, the present invention designs a nonlinear energy operator feature extraction method suitable for SSVEP brain-computer interface, and calculates the corresponding nonlinear energy for multi-channel EEG data respectively, thereby realizing the time domain feature extraction of the corresponding induced response, and the extracted feature information can be used for subsequent classification and recognition. This method provides a general feature extraction strategy, which can be further combined with the current SSVEP feature extraction or classification algorithm to improve the performance of the BCI system.

[0052] In step S110, multi-channel EEG data may be collected and data preprocessing may be performed on the multi-channel EEG data.

[0053] In the embodiment of this example, the method further includes:

[0054] The multi-channel EEG data is subjected to data preprocessing including filtering, downsampling, data segmentation, baseline correction, artifact removal, re-referencing, and elimination of bad leads or bad segments.

[0055] In step S120, the multi-channel EEG data after data preprocessing can be used to calculate the product difference with adjacent data points to generate nonlinear ability characteristics.

[0056] In the embodiment of this example, the method further includes:

[0057] Based on the fact that the product difference of adjacent data points of evoked responses in multi-channel EEG data is proportional to the amplitude and frequency of the evoked responses, the product difference with adjacent data points is calculated for the multi-channel EEG data after data preprocessing to generate nonlinear ability characteristics.

[0058] In step S130, based on the TRCA algorithm, an integrated projection matrix may be generated by calculating projection vectors according to the nonlinear capability characteristics, and classification and recognition of the nonlinear capability characteristics may be implemented based on the integrated projection matrix.

[0059] In the embodiment of this example, the TRCA algorithm in the method further includes:

[0060] Calculating the sum of the covariances between trials of the multi-channel EEG data according to the nonlinear ability characteristics;

[0061] Based on the preset constraints, the objective function is converted into an optimization problem, and the analytical solution of the objective function is calculated based on the Lagrange multiplier method;

[0062] Based on the analytical solution of the objective function, an integrated projection matrix is ​​generated.

[0063] In the embodiment of this example, the method further comprises:

[0064] The analytical solution of the objective function is the eigenvector corresponding to the maximum eigenvalue after eigendecomposing the nonlinear capability feature.

[0065] In the embodiment of this example, the method further comprises:

[0066] Based on the analytical solution of the objective function, the projection vectors of all stimulation frequencies are concatenated column-wise to obtain an integrated projection matrix.

[0067] In the embodiment of this example, the method further comprises:

[0068] The correlation coefficient between the test sample and the EEG template is calculated, and the category of the test sample is predicted based on the preset discrimination criteria.

[0069] Embodiment 2:

[0070] like Figure 2 As shown, in the embodiment of this example, the data preprocessing step includes:

[0071] The main purpose of preprocessing is to reduce the impact of noise and improve the quality of EEG signals, including filtering, downsampling, data segmentation, baseline correction, artifact removal and other steps. Filtering is to remove data in certain frequency ranges by adopting filters that meet specific requirements, so as to improve the data signal-to-noise ratio in the frequency band of interest. According to the different frequencies to be filtered and retained, it can be divided into low-pass, high-pass, band-pass and notch filters. Downsampling is to reduce the data sampling rate, which has the effect of reducing the amount of calculation and improving the operation speed. Generally, before data downsampling, the data will be input into a low-pass filter (also called an anti-aliasing filter) to suppress the high-frequency components in the signal to prevent aliasing. Data segmentation refers to intercepting continuous EEG data according to the time of the stimulus event, and then dividing it into several segments of equal length for subsequent feature analysis. Baseline correction is to subtract the baseline value of the data segment from each segmented data point. Generally, the mean value of the data within a period of time before the start of stimulation (such as 200 milliseconds (ms)) is used as the baseline value. Artifact removal is to remove specific artifacts using corresponding methods. For other types of physiological artifacts, they can be distinguished and eliminated based on biological data of other modalities. For example, artifacts related to blinking and eye movement can be identified based on electrooculogram data; artifacts caused by head movement can be identified based on accelerometer data. In addition, independent component analysis (ICA) or regression analysis can be used to eliminate electrooculogram signals. Non-physiological artifacts usually come from interference from the external environment, so the first choice is to eliminate them by changing the environmental settings, such as ensuring good contact when wearing electrodes and avoiding excessive limb movements during the experiment. Secondly, corresponding data processing methods can be used, for example, setting a 50Hz notch filter to filter out power frequency interference, etc. In addition, there are methods such as re-referencing and eliminating bad conductors or bad segments.

[0072] In the embodiment of this example, the nonlinear energy feature extraction step includes:

[0073] Assume that the preprocessed multi-channel EEG data is The data at a certain time n under a single lead is recorded as x(n). The SSVEP signal induced by external stable stimulation can be regarded as Where Ω = 2πf / f s , f is an integer multiple of the external stimulus frequency (such as fundamental frequency, double frequency, triple frequency, etc.), f s is the sampling rate of the data, is the initial phase. Then the calculation of the neighboring data points is:

[0074]

[0075] From the trigonometric identities, we can get:

[0076]

[0077] Simplifying the above formula according to the double angle formula yields:

[0078]

[0079] From the above formula, we can get:

[0080]

[0081] From the limit squeeze theorem, we know that when Ω is close to 0, sin(Ω) is equivalent to Ω. For example, Ω<π / 4, that is, f / f s When <1 / 8, the relative error loss is only about 11%. Therefore, the above formula can be simplified to:

[0082]

[0083] The above formula shows that the product difference of the adjacent data points of the evoked response is proportional to the amplitude and frequency of the evoked response. Therefore, this method can be used to extract the EEG response information induced by different stimulation frequencies for subsequent classification and recognition.

[0084] After preprocessing, the data Using the above method, the nonlinear energy characteristics corresponding to each channel can be calculated, which is recorded as According to the calculation method, N p =N t -2. The features extracted by this method can be used for subsequent classification and recognition.

[0085] In the embodiment of this example, the classification identification step includes:

[0086] Classification and recognition depends on the characteristics of EEG features and the algorithm used. This patent proposes a general SSVEP feature extraction method. The extracted features can be combined with the current mainstream decoding algorithm for classification and recognition. The following is an example of the Ensemble task-related component analysis (eTRCA) decoding algorithm.

[0087] TRCA is a classic decoding algorithm suitable for SSVEP features. Its main optimization goal is to find the optimal projection vector Make the covariance of data between different trials of the same sample the maximum. Among them, N ch Represents the number of selected leads, j1 and j2 represent lead indexes, and the value range is 1≤j1, j2≤N ch The specific process is as follows: for the multi-lead EEG data processed and extracted by the method of the present invention After projection on the projection vector, the sum of the covariances between all trials of the same category of samples is:

[0088]

[0089] Among them, h1 and h2 represent the index of the trial, N t is the number of trials. In the above formula, the matrix The elements of are:

[0090]

[0091] In order to obtain the optimal solution of the optimization objective, the following constraints are introduced:

[0092]

[0093] Therefore, the objective function can be transformed into the following optimization problem:

[0094]

[0095] Using the Lagrange multiplier method, the analytical solution of the objective function can be obtained as the matrix Q -1 The eigenvector corresponding to the maximum eigenvalue after S eigendecomposition. Finally, combined with the idea of ​​ensemble learning, the projection vectors of all stimulation frequencies are concatenated column-wise to obtain the integrated projection matrix As shown below:

[0096]

[0097] Among them, N f Represents the number of stimulation frequencies, that is, the number of all categories. The W obtained from the above formula is the optimal projection matrix found by the ensemble task-related component analysis algorithm (Ensemble TRCA, eTRCA).

[0098] For the test sample The classification and recognition process of SSVEP features using the eTRCA algorithm is as follows. First, based on the data of a certain category n in the training set By averaging it in the third dimension, we can get the EEG template For N f categories, a total of N f EEG templates; then N f The training data of each category are respectively calculated according to the TRCA algorithm to obtain the projection vectors, and then spliced ​​to obtain W; then the correlation coefficient between the test sample and the EEG template is calculated:

[0099]

[0100] It should be noted that ρ here represents the calculated two-dimensional correlation coefficient. Finally, the predicted category of the test sample is determined according to the following formula:

[0101]

[0102] From this we can get, τ t This is the predicted category of the test sample.

[0103] In the embodiment of this example, a nonlinear energy feature extraction method suitable for the SSVEP brain-computer interface adopts a nonlinear energy operator to calculate the multi-lead EEG data one by one, which can effectively extract the nonlinear energy features of the induced response, providing a new method and new idea for improving the performance of the SSVEP-BCI system.

[0104] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0105] In addition, in this exemplary embodiment, a nonlinear energy operator feature extraction device suitable for brain-computer steady-state signals is also provided. Figure 3 As shown, the nonlinear energy operator feature extraction device 300 suitable for brain-computer steady-state signals may include: a data preprocessing module 310, a feature calculation module 320 and a classification recognition module 330. Among them:

[0106] A data preprocessing module 310 is used to collect multi-channel EEG data and perform data preprocessing on the multi-channel EEG data;

[0107] The feature calculation module 320 is used to calculate the product difference between the multi-channel EEG data after data preprocessing and the adjacent data points to generate nonlinear ability features;

[0108] The classification and identification module 330 is used to generate an integrated projection matrix by calculating projection vectors based on the TRCA algorithm and according to the nonlinear capability characteristics, and to implement classification and identification of the nonlinear capability characteristics based on the integrated projection matrix.

[0109] The specific details of each of the above-mentioned nonlinear energy operator feature extraction device modules suitable for brain-computer steady-state signals have been described in detail in the corresponding nonlinear energy operator feature extraction method suitable for brain-computer steady-state signals, so they will not be repeated here.

[0110] It should be noted that although several modules or units of a nonlinear energy operator feature extraction device 300 suitable for brain-computer steady-state signals are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0111] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0112] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0113] Refer to the following Figure 4 An electronic device 400 according to such an embodiment of the present invention is described. Figure 4 The electronic device 400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0114] like Figure 4 As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: the at least one processing unit 410, the at least one storage unit 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0115] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 410 can perform the following steps: Figure 1 Steps S110 to S130 shown in FIG.

[0116] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 4201 and / or a cache storage unit 4202 , and may further include a read-only storage unit (ROM) 4203 .

[0117] The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0118] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0119] The electronic device 400 may also communicate with one or more external devices 470 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 via a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0120] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0121] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0122] refer to Figure 5 As shown, a program product 500 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0123] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0124] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0125] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0126] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0127] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0128] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0129] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A nonlinear energy operator feature extraction method suitable for brain-computer steady-state signals, characterized in that: The method comprises: Collecting multi-channel EEG data and performing data preprocessing on the multi-channel EEG data; For the multi-channel EEG data after data preprocessing, the product difference with the adjacent data points is calculated to generate nonlinear ability characteristics; Based on the TRCA algorithm, according to the nonlinear capability characteristics, an integrated projection matrix is ​​generated by calculating projection vectors, and classification and recognition of the nonlinear capability characteristics are achieved based on the integrated projection matrix.

2. The method according to claim 1, characterized in that The method further comprises: The multi-channel EEG data is subjected to data preprocessing including filtering, downsampling, data segmentation, baseline correction, artifact removal, re-referencing, and elimination of bad leads or bad segments.

3. The method according to claim 1, characterized in that The method further comprises: Based on the fact that the product difference of adjacent data points of evoked responses in multi-channel EEG data is proportional to the amplitude and frequency of the evoked responses, the product difference with adjacent data points is calculated for the multi-channel EEG data after data preprocessing to generate nonlinear ability characteristics.

4. The method according to claim 1, characterized in that The TRCA algorithm in the method further comprises: Calculating the sum of the covariances between trials of the multi-channel EEG data according to the nonlinear ability characteristics; Based on the preset constraints, the objective function is converted into an optimization problem, and the analytical solution of the objective function is calculated based on the Lagrange multiplier method; Based on the analytical solution of the objective function, an integrated projection matrix is ​​generated.

5. The method according to claim 4, characterized in that The method further comprises: The analytical solution of the objective function is the eigenvector corresponding to the maximum eigenvalue after eigendecomposing the nonlinear capability feature.

6. The method according to claim 4, characterized in that The method further comprises: Based on the analytical solution of the objective function, the projection vectors of all stimulation frequencies are concatenated column-wise to obtain an integrated projection matrix.

7. The method according to claim 1, characterized in that The method further comprises: The correlation coefficient between the test sample and the EEG template is calculated, and the category of the test sample is predicted based on the preset discrimination criteria.

8. A nonlinear energy operator feature extraction device suitable for brain-computer steady-state signals, characterized in that: The device comprises: A data preprocessing module, used for collecting multi-channel EEG data and performing data preprocessing on the multi-channel EEG data; The feature calculation module is used to calculate the product difference between the multi-channel EEG data after data preprocessing and the adjacent data points to generate nonlinear ability features; The classification and recognition module is used to generate an integrated projection matrix by calculating the projection vector based on the TRCA algorithm and the nonlinear capability characteristics, and to realize the classification and recognition of the nonlinear capability characteristics based on the integrated projection matrix.

9. An electronic device, characterized in that: include Processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images