SSVEP electroencephalogram signal recognition method and system, storage medium and electronic device

Abstract

The application provides a SSVEP electroencephalogram signal recognition method and system, a storage medium and an electronic device, which comprises the following steps: obtaining a SSVEP electroencephalogram signal; obtaining an effective SSVEP electroencephalogram signal; extracting the correlation coefficient of each sub-band of the SSVEP electroencephalogram signal for each stimulation target; performing normalization processing on the correlation coefficient to obtain a normalized correlation coefficient; calculating the credibility parameter corresponding to each sub-band of the effective SSVEP electroencephalogram signal; mapping the credibility parameter to a preset interval to obtain a mapped credibility parameter; setting a weight coefficient for different sub-bands; fine-tuning the weight coefficient based on the mapped credibility parameter to obtain an adaptive weight coefficient; calculating the sub-band correlation coefficient of the SSVEP electroencephalogram signal for each stimulation target, and selecting the stimulation target corresponding to the maximum value of the sub-band correlation coefficient as the recognition result. The SSVEP electroencephalogram signal recognition method and system, the storage medium and the electronic device of the application can realize accurate recognition of the electroencephalogram signal by adaptively adjusting the weight of different sub-bands of the electroencephalogram signal.

Description

Technical Field

[0001] This invention relates to the technical field of brain-computer interfaces, and in particular to an SSVEP brainwave signal recognition method and system, storage medium, and electronic device. Background Technology

[0002] In the field of brain-computer interfaces (BCI), electroencephalograms (EEGs), as an important way to record brain activity, have become one of the main data sources for researchers to realize human-computer interaction and brain control technologies. The development of BCI technology provides unique communication and control methods, and also shows broad application potential in fields such as medicine, virtual reality, and entertainment.

[0003] Canonical Correlation Analysis (CCA) is a statistical technique used to explore relationships between multivariate data. In brain-computer interfaces, CCA has been widely used to extract information from electroencephalogram (EEG) signals to support task classification and recognition. However, traditional CCA methods do not take into account the differences in correlation between different frequency bands of EEG signals. Different frequency bands of EEG signals may reflect different patterns of neural activity and task characteristics. Therefore, utilizing the correlations between these frequency bands can lead to more accurate classification and prediction.

[0004] Filter Bank Canonical Correlation Analysis (FBCCA), as an extended approach, offers a solution to this problem. By decomposing EEG signals into sub-bands of different frequency bands and applying standard CCA to each sub-band, FBCCA effectively captures the correlations between frequency bands. Compared to traditional CCA, FBCCA exhibits higher classification performance in brain-computer interface tasks, extracting more task-specific information.

[0005] However, FBCCA still faces some problems and limitations in practical applications. One of its main problems is that the weights of each subband are usually fixed, meaning the algorithm cannot adequately adapt to different types of EEG signals. The reliability of different subband components in EEG signals varies, and signals from different frequency bands may have different characteristics and information content. Fixed weight settings can lead to inconsistent algorithm performance across different tasks or individuals. This limits the effectiveness of FBCCA in processing diverse EEG signals. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an SSVEP EEG signal recognition method and system, storage medium and electronic device, which achieves accurate recognition of EEG signals by adaptively adjusting the weights of different subbands of the EEG signal when applying the FBCCA algorithm.

[0007] In a first aspect, the present invention provides a method for recognizing SSVEP brainwave signals, the method comprising the following steps: acquiring SSVEP brainwave signals; preprocessing the SSVEP brainwave signals to obtain valid SSVEP brainwave signals; and extracting the correlation coefficients of each sub-band of the valid SSVEP brainwave signals for each stimulus target based on a filter bank canonical correlation analysis algorithm. Where k = 1, 2, ..., K, n = 1, 2, ..., N, k is the index of the stimulus target, K is the number of stimulus targets, n is the index of the subband, and N is the number of subbands, n = 1, 2, ..., N; for the correlation coefficient Perform normalization to obtain the normalized correlation coefficient. Calculate the confidence parameters corresponding to each subband of the valid SSVEP EEG signal. Where max is the maximum value and 2ndmax is the second largest value; the confidence parameter γ is... n Map to a preset range and obtain the mapping confidence parameter. For different sub-bands, set the weighting coefficient w(n) = n -1.25 +0.25; based on the mapping confidence parameter Fine-tuning the weight coefficient w(n) yields adaptive weight coefficients. Calculate the subband correlation coefficients of the SSVEP EEG signals for each stimulus target. The stimulus target corresponding to the maximum value of the subband correlation coefficient is selected as the recognition result.

[0008] In one implementation of the first aspect, acquiring SSVEP EEG signals includes the following steps:

[0009] In displaying the target stimulus paradigm;

[0010] SSVEP brainwave signals generated when subjects view the target stimulus paradigm are collected using an EEG acquisition device.

[0011] In one implementation of the first aspect, preprocessing the SSVEP EEG signal includes the following steps:

[0012] The SSVEP EEG signal was downsampled to 250Hz;

[0013] Bandpass filtering and 50Hz notch filtering were applied to the downsampled SSVEP EEG signal.

[0014] In one implementation of the first aspect, extracting the correlation coefficients of each sub-band of the effective SSVEP EEG signal with respect to each stimulus target includes the following steps:

[0015] The filter bank, based on the filter bank canonical correlation analysis algorithm, decomposes the effective SSVEP EEG signal into multiple sub-bands;

[0016] For each subband, canonical correlation analysis is performed for each stimulus target to obtain the corresponding correlation coefficient.

[0017] In one implementation of the first aspect, according to Obtain the normalized correlation coefficient.

[0018] In one implementation of the first aspect, according to Obtain the mapping credibility parameter Where [a,b] is a preset interval, γ max and γ min These represent the credibility parameter γ. n The maximum and minimum values ​​in the range.

[0019] In one implementation of the first aspect, according to Obtain the adaptive weight coefficients.

[0020] In a second aspect, the present invention provides an SSVEP EEG signal recognition system, the system comprising an acquisition module, a preprocessing module, a correlation analysis module, a normalization module, a reliability module, a mapping module, a setting module, an adaptive module, and a recognition module;

[0021] The acquisition module is used to acquire SSVEP EEG signals;

[0022] The preprocessing module is used to preprocess the SSVEP EEG signal to obtain a valid SSVEP EEG signal.

[0023] The correlation analysis module is used to extract the correlation coefficients of each sub-band of the effective SSVEP EEG signal with respect to each stimulus target based on the filter bank canonical correlation analysis algorithm. Where k = 1, 2, ..., K, n = 1, 2, ..., N, k is the index of the stimulus target, K is the number of stimulus targets, n is the index of the subband, and N is the number of subbands, n = 1, 2, ..., N;

[0024] The normalization module is used to normalize the correlation coefficient. Perform normalization to obtain the normalized correlation coefficient.

[0025] The credibility module is used to calculate the credibility parameters corresponding to each subband of the valid SSVEP EEG signal. Where max is the maximum value and 2ndmax is the second largest value;

[0026] The mapping module is used to map the confidence parameter γ n Map to a preset range and obtain the mapping confidence parameter.

[0027] The setting module is used to set the weighting coefficient w(n) = n for different sub-bands. -1.25 +0.25;

[0028] The adaptive module is used to base on the mapping confidence parameter. Fine-tuning the weight coefficient w(n) yields adaptive weight coefficients.

[0029] The recognition module is used to calculate the sub-band correlation coefficient of the SSVEP EEG signal for each stimulus target. The stimulus target corresponding to the maximum value of the subband correlation coefficient is selected as the recognition result.

[0030] Thirdly, the present invention provides a storage medium on which a computer program is stored, which, when executed by a processor, implements the above-described SSVEP EEG signal recognition method.

[0031] Fourthly, the present invention provides an electronic device, comprising: a processor and a memory;

[0032] The memory is used to store computer programs;

[0033] The processor is used to execute the computer program stored in the memory, so that the electronic device performs the SSVEP EEG signal recognition method described above.

[0034] Fifthly, the present invention provides an SSVEP brainwave signal recognition system, including a brainwave signal acquisition device and the aforementioned electronic device;

[0035] The EEG signal acquisition device is used to acquire EEG signals and send the EEG signals to the electronic device.

[0036] As described above, the SSVEP EEG signal recognition method and system, storage medium, and electronic device of the present invention have the following beneficial effects:

[0037] (1) By optimizing the FBCCA algorithm, the reliability of each sub-band component is used to adapt the sub-band weight coefficient, thereby enhancing the adaptability of the brain-computer interface system to different types of EEG signals and improving the recognition accuracy of EEG signals.

[0038] (2) By setting adaptive weight coefficients, the correlation of different subbands is effectively weighed, overcoming the problem that the reliability and information content of different EEG data harmonic components are different; by fine-tuning the weight coefficients according to the reliability, it can better adapt to different subjects and different experimental conditions, thereby further improving the recognition accuracy and robustness.

[0039] (3) It can better adapt to different types of EEG signals, which helps to improve the stability and performance of brain-computer interface systems and further expand their application prospects in the fields of medicine, assistive technology and entertainment. Attached Figure Description

[0040] Figure 1 The flowchart shown is an embodiment of the SSVEP EEG signal recognition method of the present invention;

[0041] Figure 2 The diagram shows a framework schematic of the SSVEP EEG signal recognition method of the present invention in one embodiment;

[0042] Figure 3 The diagram shown is a schematic representation of the stimulation paradigm adopted in this invention in one embodiment;

[0043] Figure 4 The diagram shown is a structural schematic of the SSVEP EEG signal recognition system of the present invention in one embodiment.

[0044] Figure 5 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0045] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0046] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0047] The following embodiments of the present invention provide an SSVEP brainwave signal recognition method, which can be applied to electronic devices. The electronic devices described in this invention may include mobile phones with wireless charging capabilities, tablet computers, laptops, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. The embodiments of the present invention do not impose any limitations on the specific type of electronic device.

[0048] For example, the electronic device may be a station (STAION, ST) in a WLAN with wireless charging capability, a cellular phone, cordless phone, Session Initiation Protocol (SIP) phone, Wireless Local Loop (WLL) station, Personal Digital Assistant (PDA) device, handheld device with wireless charging capability, computing device or other processing device, computer, laptop computer, handheld communication device, handheld computing device, and / or other devices for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks, mobile terminals in future evolved Public Land Mobile Networks (PLMNs), or mobile terminals in future evolved Non-terrestrial Networks (NTNs).

[0049] For example, the electronic device can communicate with networks and other devices wirelessly. The wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technologies. The GNSS can include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0050] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.

[0051] like Figure 1 and Figure 2 As shown, in one embodiment, the SSVEP EEG signal recognition method of the present invention includes steps S1-S9.

[0052] Step S1: Obtain SSVEP EEG signals.

[0053] Specifically, steady-state visual evoked potentials (SSVEPs) are a special type of visual evoked potential. They are evoked by visual stimuli with a fixed frequency. When visual stimuli are presented periodically at a specific frequency (such as flashing, image flipping, image scaling, etc.), the visual system is affected and produces an evoked response with stable frequency characteristics. This evoked response typically contains a frequency component of the same frequency as the stimulus and its higher harmonic components.

[0054] In this invention, a selected SSVEP stimulation paradigm is used to display flashing stimuli on a screen, and the SSVEP generated by the subject while viewing the stimulus is collected using an EEG acquisition device. For example, using a method such as... Figure 3 The 40 target stimulus interfaces shown are used as a stimulus paradigm. It should be noted that other stimulus paradigms can also be used, and this invention does not impose detailed restrictions on the stimulus paradigm.

[0055] In one embodiment, the sampling rate for acquiring EEG signals is 1000 Hz. Using the 10-20 system electrode placement standard, nine electrodes (Pz, PO5, PO3, POz, PO4, PO6, O1, Oz, and O2) are placed in the parietal and occipital lobe regions to record SSVEP signals. A reference electrode is located at the apex (Cz) position, and the electrode impedance is kept below 10 kΩ.

[0056] Step S2: Preprocess the SSVEP EEG signal to obtain a valid SSVEP EEG signal.

[0057] Specifically, the SSVEP EEG signal is preprocessed to remove noise and highlight the effective signal. This includes downsampling the SSVEP EEG signal to 250Hz to reduce computational complexity, applying bandpass filtering to retain the signal within the frequency range of interest, and applying a 50Hz notch filter to eliminate power line interference.

[0058] Step S3: Based on the filter bank canonical correlation analysis algorithm, extract the correlation coefficients of each sub-band of the effective SSVEP EEG signal with respect to each stimulus target. Where k = 1, 2, ..., K, n = 1, 2, ..., N, k is the index of the stimulus target, K is the number of stimulus targets, n is the index of the subband, and N is the number of subbands, n = 1, 2, ..., N.

[0059] Specifically, a filter bank canonical correlation analysis algorithm is used to extract EEG signal features related to the stimulation frequency of the stimulus target, i.e., the correlation coefficients of each sub-band of the effective SSVEP EEG signal with respect to each stimulus target.

[0060] First, a filter bank using the filter bank canonical correlation analysis algorithm decomposes the effective SSVEP EEG signal into multiple sub-bands (SB1, SB2…SB). N Then, for each subband, canonical correlation analysis is performed for each stimulus target to obtain the corresponding correlation coefficient.

[0061] Step S4: Calculate the correlation coefficient. Perform normalization to obtain the normalized correlation coefficient.

[0062] Specifically, L1 normalization is performed on each correlation coefficient group. Since all correlation coefficients ρ are positive, their absolute values ​​can be ignored. Therefore, according to... Obtain the normalized correlation coefficient.

[0063] Step S5: Calculate the confidence parameters corresponding to each subband of the valid SSVEP EEG signal. Where max is the maximum value and 2ndmax is the second largest value.

[0064] Specifically, the confidence parameter γ is calculated based on the cross-entropy loss function. n The credibility parameter reflects the credibility level; γ is always negative, and the smaller γ is, the higher the credibility level.

[0065] Step S6: Set the confidence parameter γ n Map to a preset range and obtain the mapping confidence parameter.

[0066] Specifically, according to Obtain the mapping credibility parameter Where [a,b] is a preset interval, γ max and γ min These represent the credibility parameter γ. n The maximum and minimum values ​​in the range. The preset range can be set according to actual needs.

[0067] Step S7: Set the weighting coefficient w(n) = n for different sub-bands. -1.25 +0.25.

[0068] Specifically, different weighting coefficients will be assigned to different sub-bands. Since the signal-to-noise ratio of SSVEP decreases with increasing harmonic order, the weighting coefficient w(n) is defined as w(n) = n -1.25 +0.25.

[0069] Step S8: Based on the mapping confidence parameter Fine-tuning the weight coefficient w(n) yields adaptive weight coefficients.

[0070] Specifically, in the canonical correlation analysis algorithm for filter banks, the weight coefficients are fixed. However, since the confidence levels of each subband vary, the weight coefficients can be fine-tuned using the confidence level. Higher confidence levels result in increased weights, and vice versa. Therefore, according to... Obtain the adaptive weight coefficients.

[0071] Step S9: Calculate the sub-band correlation coefficient of the SSVEP EEG signal for each stimulus target. The stimulus target corresponding to the maximum value of the subband correlation coefficient is selected as the recognition result.

[0072] Specifically, for each stimulus target, the correlation coefficients of all sub-bands are weighted and summed. The stimulus target corresponding to the highest value of the sub-band correlation coefficient is selected as the identification result. And the corresponding stimulation frequency can be obtained.

[0073] The SSVEP EEG signal recognition method of the present invention will be further illustrated below through specific embodiments.

[0074] In this embodiment, firstly select Figure 3 The target stimulus interface is shown. A flashing stimulus is then displayed on the screen, and the SSVEP generated by the subject while viewing the stimulus is acquired using an EEG acquisition device. For example, a NeuroscanSynAmps 264-256-channel EEG acquisition system is used. The sampling rate for acquiring EEG signals is 1000 Hz, and the electrode arrangement conforms to the 10-20 system electrode arrangement standard. Nine electrodes (Pz, PO5, PO3, POz, PO4, PO6, O1, Oz, and O2) are placed in the parietal and occipital lobe regions to record SSVEP EEG signals. The reference electrode is located at the apex (Cz) position, and the electrode impedance is kept below 10 kΩ.

[0075] After obtaining the SSVEP EEG signal, it needs to be preprocessed to remove noise and highlight the effective signal. Preprocessing includes downsampling the EEG signal to 250Hz to reduce computational complexity and applying a 50Hz notch filter to eliminate power line interference. Since the FBCCA algorithm includes a filter bank, bandpass filtering may not be necessary during preprocessing.

[0076] After preprocessing, the standard FBCCA algorithm was used to extract EEG signal features related to the stimulus frequency. First, a standard filter bank was used to decompose the EEG signal into five sub-bands with different frequency ranges. Then, standard canonical correlation analysis was performed on each sub-band, generating a set of correlation coefficients for each sub-band. k = 1, 2, ..., 40, n = 1, 2, ..., 5. Where k is the index of the stimulus target and n is the index of the subband.

[0077] Performing L1 normalization on each correlation coefficient group, since all correlation coefficients ρ are positive, the absolute values ​​can be ignored, resulting in: k=1,2,…,40, n=1,2,…,5.

[0078] Calculate the confidence parameter n = 1, 2, ..., 5.

[0079] Different weighting coefficients will be assigned to different sub-bands. Since the signal-to-noise ratio of SSVEP decreases with increasing harmonic order, the weighting coefficient w(n) is defined as w(n) = n -1.25 +0.25, n=1,2,…,5.

[0080] use Fine-tuning the weighting coefficients yields adaptive weighting coefficients. n = 1, 2, ..., 5.

[0081] γ n Mapping to the interval [-0.5, 0] yields n = 1, 2, ..., 5.

[0082] The subband correlation coefficient can be obtained by summing the weighted squares of all subband correlation coefficients. k = 1, 2, ..., 40.

[0083] Finally, the target with the largest subband correlation coefficient is selected as the identification result, which corresponds to the most likely stimulus frequency.

[0084]

[0085] The scope of protection of the SSVEP EEG signal recognition method described in this embodiment is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principle of this invention is included within the scope of protection of this invention.

[0086] This invention also provides an SSVEP EEG signal recognition system, which can implement the SSVEP EEG signal recognition method described in this invention. However, the implementation device of the SSVEP EEG signal recognition system described in this invention includes, but is not limited to, the structure of the SSVEP EEG signal recognition system listed in this embodiment. All structural modifications and substitutions of the prior art made in accordance with the principles of this invention are included within the protection scope of this invention.

[0087] like Figure 4 As shown, in one embodiment, the SSVEP EEG signal recognition system of the present invention includes an acquisition module 41, a preprocessing module 42, a correlation analysis module 43, a normalization module 44, a reliability module 45, a mapping module 46, a setting module 47, an adaptive module 48, and a recognition module 49.

[0088] The acquisition module 41 is used to acquire SSVEP EEG signals.

[0089] The preprocessing module 42 is connected to the acquisition module 41 and is used to preprocess the SSVEP EEG signal to obtain a valid SSVEP EEG signal.

[0090] The correlation analysis module 43 is connected to the preprocessing module 42 and is used to extract the correlation coefficients of each sub-band of the effective SSVEP EEG signal with respect to each stimulus target based on the filter bank canonical correlation analysis algorithm. Where k = 1, 2, ..., K, n = 1, 2, ..., N, k is the index of the stimulus target, K is the number of stimulus targets, n is the index of the subband, and N is the number of subbands, n = 1, 2, ..., N.

[0091] The normalization module 44 is connected to the correlation analysis module 43 and is used to normalize the correlation coefficient. Perform normalization to obtain the normalized correlation coefficient.

[0092] The reliability module 45 is connected to the normalization module 44 and is used to calculate the reliability parameters corresponding to each subband of the valid SSVEP EEG signal. Where max is the maximum value and 2ndmax is the second largest value.

[0093] The mapping module 46 is connected to the credibility module 45 and is used to map the credibility parameter γ. n Map to a preset range and obtain the mapping confidence parameter.

[0094] The setting module 47 is used to set the weighting coefficient w(n) = n for different sub-bands. -1.25 +0.25.

[0095] The adaptive module 48 is connected to the credibility module 45 and the setting module 47, and is used to base the mapping credibility parameter. Fine-tuning the weight coefficient w(n) yields adaptive weight coefficients.

[0096] The identification module 49 is connected to the correlation analysis module 43 and the adaptive module 48, and is used to calculate the sub-band correlation coefficient of the SSVEP EEG signal for each stimulus target. The stimulus target corresponding to the maximum value of the subband correlation coefficient is selected as the recognition result.

[0097] The structure and principle of the acquisition module 41, preprocessing module 42, correlation analysis module 43, normalization module 44, credibility module 45, mapping module 46, setting module 47, adaptive module 48 and recognition module 49 correspond one-to-one with the steps in the SSVEP EEG signal recognition method described above, so they will not be repeated here.

[0098] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0099] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0100] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0101] This invention also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the SSVEP EEG signal recognition method of the above embodiments can be executed by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0102] This invention also provides an electronic device. The electronic device includes a processor and a memory.

[0103] The memory is used to store computer programs.

[0104] The memory includes various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0105] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device performs the SSVEP EEG signal recognition method described above.

[0106] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0107] like Figure 5As shown, the electronic device of the present invention is embodied in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units 51, a memory 52, and a bus 53 connecting different system components (including the memory 52 and the processing unit 51).

[0108] Bus 53 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0109] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0110] Memory 52 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 522. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 523 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 53 via one or more data media interfaces. Memory 52 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0111] A program / utility 524 having a set (at least one) of program modules 5241 may be stored, for example, in memory 52. ​​Such program modules 5241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 5241 typically perform the functions and / or methods described in the embodiments of the present invention.

[0112] The electronic device can also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network interface card, modem, etc.). This communication can be performed through input / output (I / O) interface 54. Furthermore, the electronic device can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 55. Figure 5 As shown, network adapter 55 communicates with other modules of the electronic device via bus 53. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0113] In one embodiment, the SSVEP EEG signal recognition system of the present invention includes an EEG signal acquisition device and the aforementioned electronic device.

[0114] The EEG signal acquisition device is used to acquire EEG signals and transmit the EEG signals to the electronic device. The EEG signal acquisition device and the electronic device transmit signals via wired or wireless means.

[0115] In summary, the SSVEP EEG signal recognition method, system, storage medium, and electronic device of this invention acquire the energy weights of EEG signals at different frequencies by collecting EEG data from subjects in focused and unfocused states, thereby training an SSVEP EEG signal recognition model. This model is then used to recognize the subject's SSVEP EEG signals. The invention takes into account the influence of the energy of EEG signals at different frequencies within the frequency band on focus levels, improving the accuracy of focus monitoring. Furthermore, it considers the differences in SSVEP EEG signal recognition models between individuals, enhancing the usability of SSVEP EEG signal recognition. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and possesses high industrial applicability.

[0116] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for recognizing SSVEP brainwave signals, characterized in that, The method includes the following steps: Acquire SSVEP EEG signals; The SSVEP EEG signals are preprocessed to obtain valid SSVEP EEG signals; Based on the filter bank canonical correlation analysis algorithm, the correlation coefficients of each sub-band of the effective SSVEP EEG signal with respect to each stimulus target are extracted. ,in , The sequence number of the stimulus target. The number of stimulating targets, This is the sequence number of the sub-band. The number of sub-bands; Regarding the correlation coefficient Perform normalization to obtain the normalized correlation coefficient. ; Calculate the confidence parameters corresponding to each subband of the valid SSVEP EEG signal. ,in The maximum value, This is the second largest value; The credibility parameter Map to a preset range and obtain the mapping confidence parameter. ; Set weighting coefficients for different sub-bands. ; Based on the mapping reliability parameter For the weighting coefficients Fine-tune the settings to obtain adaptive weight coefficients. ; Calculate the subband correlation coefficients of the SSVEP EEG signals for each stimulus target. The stimulus target corresponding to the maximum value of the subband correlation coefficient is selected as the recognition result.

2. The SSVEP EEG signal recognition method according to claim 1, characterized in that, Obtaining SSVEP EEG signals includes the following steps: Displaying the target stimulus paradigm; SSVEP brainwave signals generated when subjects view the target stimulus paradigm are collected using an EEG acquisition device.

3. The SSVEP EEG signal recognition method according to claim 1, characterized in that, Preprocessing the SSVEP EEG signal includes the following steps: The SSVEP EEG signal was downsampled to 250Hz; Bandpass filtering and 50Hz notch filtering were applied to the downsampled SSVEP EEG signal.

4. The SSVEP EEG signal recognition method according to claim 1, characterized in that, Extracting the correlation coefficients of each subband of the effective SSVEP EEG signal with respect to each stimulus target includes the following steps: The filter bank, based on the filter bank canonical correlation analysis algorithm, decomposes the effective SSVEP EEG signal into multiple sub-bands; For each subband, canonical correlation analysis is performed for each stimulus target to obtain the corresponding correlation coefficient.

5. The SSVEP EEG signal recognition method according to claim 1, characterized in that, according to Obtain the normalized correlation coefficient.

6. The SSVEP EEG signal recognition method according to claim 1, characterized in that, according to Obtain the mapping credibility parameter Where [a, b] is a preset interval. and These represent the credibility parameters. The maximum and minimum values ​​in the range.

7. The SSVEP EEG signal recognition method according to claim 1, characterized in that, according to Obtain the adaptive weight coefficients.

8. An SSVEP brainwave signal recognition system, characterized in that, The system includes an acquisition module, a preprocessing module, a correlation analysis module, a normalization module, a credibility module, a mapping module, a setting module, an adaptive module, and an identification module; The acquisition module is used to acquire SSVEP EEG signals; The preprocessing module is used to preprocess the SSVEP EEG signal to obtain a valid SSVEP EEG signal. The correlation analysis module is used to extract the correlation coefficients of each sub-band of the effective SSVEP EEG signal with respect to each stimulus target based on the filter bank canonical correlation analysis algorithm. ,in , The sequence number of the stimulus target. The number of stimulating targets, This is the sequence number of the sub-band. The number of sub-bands; The normalization module is used to normalize the correlation coefficient. Perform normalization to obtain the normalized correlation coefficient. ; The credibility module is used to calculate the credibility parameters corresponding to each subband of the valid SSVEP EEG signal. ,in The maximum value, This is the second largest value; The mapping module is used to map the credibility parameter. Map to a preset range and obtain the mapping confidence parameter. ; The setting module is used to set weighting coefficients for different sub-bands. ; The adaptive module is used to base on the mapping confidence parameter. For the weighting coefficients Fine-tune the settings to obtain adaptive weight coefficients. ; The recognition module is used to calculate the sub-band correlation coefficient of the SSVEP EEG signal for each stimulus target. The stimulus target corresponding to the maximum value of the subband correlation coefficient is selected as the recognition result.

9. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the SSVEP EEG signal recognition method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to cause the electronic device to perform the SSVEP EEG signal recognition method according to any one of claims 1 to 7.

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