SSVEP electroencephalogram signal identification method and system, storage medium and electronic equipment

Abstract

The invention provides an SSVEP electroencephalogram signal recognition method and system, a storage medium and electronic equipment. The SSVEP electroencephalogram signal recognition method comprises the steps that SSVEP electroencephalogram signals are obtained; effective SSVEP electroencephalogram signals are obtained; extracting a 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 a credibility parameter corresponding to each sub-band of the effective SSVEP electroencephalogram signal; mapping the credibility parameter to a preset interval to obtain a mapping credibility parameter; weight coefficients are set for different sub-bands; performing fine adjustment on the weight coefficient based on a mapping credibility parameter to obtain an adaptive weight coefficient; and 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 an identification result. According to the SSVEP electroencephalogram signal recognition method and system, the storage medium and the electronic equipment, accurate recognition of the electroencephalogram signals is achieved by adaptively adjusting the weights of the different sub-bands of the electroencephalogram signals.

Description

Technical Field

[0001] The present invention relates to the technical field of brain-computer interfaces, and in particular to a SSVEP electroencephalogram (ESVEP) signal recognition method and system, a storage medium, and an electronic device. Background Art

[0002] In the field of brain-computer interface (BCI), electroencephalogram (EEG), as an important way to record brain activity, has become one of the main data sources used by researchers to achieve human-computer interaction and brain control technology. The development of BCI technology provides a unique way of communication and control for people with disabilities, and also has a wide range of application potentials in the fields of medicine, virtual reality, entertainment, etc.

[0003] Canonical Correlation Analysis (CCA) is a statistical technique for exploring the relationship between multivariate data. In brain-computer interfaces, CCA has been widely used to mine information from EEG signals to support task classification and recognition. However, the traditional CCA method does not take into account the differences in the correlation between EEG signals in different frequency bands. EEG signals in different frequency bands may reflect different neural activity patterns and task characteristics. Therefore, using the correlation between these frequency bands can enable more accurate classification predictions.

[0004] Filter Bank Canonical Correlation Analysis (FBCCA) is an extended method that provides a solution to this problem. By decomposing the EEG signal into sub-band components of different frequency bands and applying standard CCA to each sub-band component, FBCCA can effectively capture the correlation between frequency bands. Compared with traditional CCA, FBCCA shows higher classification performance in brain-computer interface tasks and can extract more information related to specific tasks.

[0005] However, FBCCA still has some problems and limitations in practical applications. One of the main problems is that the weight of each sub-band is usually fixed, which means that the algorithm cannot fully adapt to different types of EEG signals. The credibility of different sub-band components in EEG signals varies, and signals in different frequency bands may have different characteristics and information. Fixed weight settings may cause the algorithm to perform unstable in 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 mentioned above, the purpose of the present invention is to provide a SSVEP EEG signal recognition method and system, a storage medium and an electronic device, which can realize accurate recognition of EEG signals by adaptively adjusting the weights of different sub-bands of EEG signals when applying the FBCCA algorithm.

[0007] In a first aspect, the present invention provides a method for identifying SSVEP electroencephalogram (EEG) signals, the method comprising the following steps: obtaining an SSVEP electroencephalogram (EEG) signal; preprocessing the SSVEP electroencephalogram (EEG) signal to obtain an effective SSVEP electroencephalogram (EEG) signal; extracting the correlation coefficients of each subband of the effective SSVEP electroencephalogram (EEG) signal 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 number of stimulus targets, K is the number of stimulus targets, n is the number of sub-bands, N is the number of sub-bands, n = 1, 2, ..., N; for the correlation coefficient Perform normalization processing to obtain the normalized correlation coefficient Calculate the credibility parameters corresponding to each sub-band of the effective SSVEP EEG signal Where max is the maximum value, 2ndmax is the second largest value; the credibility parameter γ n Map to the preset interval and obtain the mapping credibility parameters For different subbands, set the weight coefficient w(n) = n -1.25 +0.25; based on the mapping confidence parameter Fine-tune the weight coefficient w(n) to obtain an adaptive weight coefficient Calculate the sub-band correlation coefficient of the SSVEP EEG signal for each stimulation target The stimulus target corresponding to the maximum value of the sub-band correlation coefficient is selected as the recognition result.

[0008] In an implementation of the first aspect, obtaining the SSVEP electroencephalogram signal includes the following steps:

[0009] In the display target stimulus paradigm;

[0010] The SSVEP electroencephalogram (EEG) signal generated when the subject watches the target stimulation paradigm is collected based on the EEG acquisition device.

[0011] In an implementation of the first aspect, preprocessing the SSVEP electroencephalogram signal comprises the following steps:

[0012] Downsampling the SSVEP electroencephalogram signal to 250 Hz;

[0013] The downsampled SSVEP EEG signal was subjected to bandpass filtering and 50 Hz notch filtering.

[0014] In an implementation of the first aspect, extracting the correlation coefficient of each sub-band of the effective SSVEP electroencephalogram signal for each stimulation target comprises the following steps:

[0015] Decomposing the effective SSVEP electroencephalogram signal into a plurality of sub-bands by using a filter bank based on a filter bank canonical correlation analysis algorithm;

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

[0017] In an implementation of the first aspect, according to The normalized correlation coefficient is obtained.

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

[0019] In an implementation of the first aspect, according to Get the adaptive weight coefficient.

[0020] In a second aspect, the present invention provides a SSVEP electroencephalogram signal recognition system, the system comprising 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 a recognition module;

[0021] The acquisition module is used to acquire SSVEP electroencephalogram 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 for each stimulation target based on the filter bank canonical correlation analysis algorithm. Where k = 1, 2, ..., K, n = 1, 2, ..., N, k is the number of stimulus targets, K is the number of stimulus targets, n is the number of sub-bands, N is the number of sub-bands, n = 1, 2, ..., N;

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

[0025] The credibility module is used to calculate the credibility parameters corresponding to each sub-band of the effective SSVEP EEG signal Among them, max is the maximum value and 2ndmax is the second largest value;

[0026] The mapping module is used to transform the credibility parameter γ n Map to the preset interval and obtain the mapping credibility parameters

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

[0028] The adaptive module is used to map the credibility parameters based on the Fine-tune the weight coefficient w(n) to obtain an adaptive weight coefficient

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

[0030] In a third aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned SSVEP electroencephalogram signal recognition method.

[0031] In a fourth aspect, 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 above-mentioned SSVEP electroencephalogram signal recognition method.

[0034] In a fifth aspect, the present invention provides a SSVEP electroencephalogram signal recognition system, comprising an electroencephalogram signal acquisition device and the above-mentioned 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 and using the credibility of each sub-band component to adapt the sub-band weight coefficient, the adaptability of the brain-computer interface system to different types of EEG signals is enhanced, thereby improving the recognition accuracy of EEG signals;

[0038] (2) By setting adaptive weight coefficients, the correlation of different sub-bands is effectively weighed, overcoming the problem that the credibility and information content of harmonic components of different EEG data are different; by fine-tuning the weight coefficients according to the credibility, 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 the brain-computer interface system and further expand its application prospects in the fields of medicine, assistive technology, and entertainment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Shown is a flow chart of an SSVEP EEG signal recognition method in one embodiment of the present invention;

[0041] Figure 2 Shown is a schematic diagram of the framework of the SSVEP electroencephalogram signal recognition method of the present invention in one embodiment;

[0042] Figure 3 A schematic diagram showing a stimulation paradigm used in one embodiment of the present invention;

[0043] Figure 4 Shown is a schematic diagram of the structure of an SSVEP electroencephalogram signal recognition system in one embodiment of the present invention;

[0044] Figure 5 It is a schematic structural diagram of an electronic device of the present invention in one embodiment. DETAILED DESCRIPTION

[0045] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0046] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0047] The following embodiments of the present invention provide a SSVEP EEG signal recognition method, which can be applied to electronic devices. The electronic devices described in the present invention may include mobile phones with wireless charging function, tablet computers, laptop computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiments of the present invention do not impose any restrictions on the specific types of electronic devices.

[0048] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN).

[0049] For example, the electronic device can communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobilecommunication (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 technology. 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 in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

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

[0052] Step S1, obtaining SSVEP electroencephalogram signals.

[0053] Specifically, Steady-State Visual Evoked Potential (SSVEP) is a special type of visual evoked potential. It is induced by visual stimulation with a fixed frequency. When visual stimulation is presented periodically at a specific frequency (such as flickering, graphic flipping, graphic scaling, etc.), the visual system will be affected and produce an evoked response with stable frequency characteristics. This evoked response usually contains the same frequency component as the stimulation frequency and its higher harmonic components.

[0054] In the present invention, a SSVEP stimulation paradigm is selected to display flickering stimulation on a display screen, and the SSVEP generated by the subject when viewing the stimulation is collected by an EEG acquisition device. Figure 3 The target stimulation interface 40 shown is used as a stimulation paradigm. It should be noted that other stimulation paradigms can also be used, and the present invention does not impose any specific restrictions on the stimulation paradigm.

[0055] In one embodiment, the sampling rate of the EEG signal is 1000 Hz, and 9 electrodes (Pz, PO5, PO3, POz, PO4, PO6, O1, Oz and O2) are placed in the parietal and occipital regions to record SSVEP signals using the 10-20 system electrode arrangement standard. The reference electrode is located at the vertex (Cz) position, and the impedance of the electrode is kept below 10k.

[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 effective signals. The SSVEP EEG signal is first downsampled to 250 Hz to reduce computational complexity, then band-pass filtered to retain signals within the frequency range of interest, and 50 Hz notch filtered to eliminate power line interference.

[0058] Step S3: extracting the correlation coefficients of each sub-band of the effective SSVEP EEG signal for each stimulation target based on the filter bank canonical correlation analysis algorithm Wherein k = 1, 2, ..., K, n = 1, 2, ..., N, k is the serial number of the stimulus target, K is the number of stimulus targets, n is the serial number of the subband, N is the number of subbands, n = 1, 2, ..., N.

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

[0060] The effective SSVEP EEG signal is first decomposed into multiple sub-bands (SB) by using a filter bank of a filter bank canonical correlation analysis algorithm. 1 , SB 2 …SB N ). Then, for each sub-band, a canonical correlation analysis is performed for each stimulus target to obtain the corresponding correlation coefficient

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

[0062] Specifically, each group of correlation coefficients is L1 normalized. Since all correlation coefficients ρ are positive, their absolute values ​​can be ignored. The normalized correlation coefficient is obtained.

[0063] Step S5: Calculate the credibility parameters corresponding to each sub-band of the effective SSVEP EEG signal Among them, max is the maximum value and 2ndmax is the second largest value.

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

[0065] Step S6: The credibility parameter γ n Map to the preset interval and obtain the mapping credibility parameters

[0066] Specifically, according to Get the mapping credibility parameter Where [a,b] is the preset interval, γ max and γ min Represent the credibility parameter γ n The preset interval can be set according to actual needs.

[0067] Step S7: For different sub-bands, set the weight coefficient w(n)=n -1.25 +0.25.

[0068] Specifically, different weight coefficients are assigned to different subbands. Since the signal-to-noise ratio of SSVEP decreases with the increase of harmonic order, the weight coefficient w(n) is defined as w(n)=n -1.25 +0.25.

[0069] Step S8: Based on the mapping credibility parameter Fine-tune the weight coefficient w(n) to obtain an adaptive weight coefficient

[0070] Specifically, in the filter bank canonical correlation analysis algorithm, the weight coefficient is fixed, but since the credibility of each sub-band is different, the credibility can be used to fine-tune the weight coefficient. If the credibility is high, the weight is increased; otherwise, the weight is reduced. Get the adaptive weight coefficient.

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

[0072] Specifically, for each stimulus target, the weighted square sum of the correlation coefficients of all sub-bands is performed, and the stimulus target corresponding to the maximum value of the sub-band correlation coefficient is selected as the recognition result, that is, And the corresponding stimulation frequency can be obtained.

[0073] The SSVEP electroencephalogram signal recognition method of the present invention is further described below through specific examples.

[0074] In this embodiment, first select Figure 3 The target stimulation interface is shown. Then the flickering stimulation is displayed on the display screen, and the SSVEP generated by the subject when watching the stimulation is collected by the EEG acquisition device. For example, the Neuroscan SynAmps264-256 EEG acquisition system is used. The sampling rate of the EEG signal is 1000Hz, 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 regions to record SSVEP EEG signals. The reference electrode is located at the vertex (Cz) position, and the impedance of the electrode is kept below 10k.

[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, applying 50Hz notch filtering to eliminate power line interference, etc. Since the FBCCA algorithm includes a filter bank, bandpass filtering is no longer required in the preprocessing process.

[0076] After preprocessing, the standard FBCCA algorithm is used to extract EEG signal features related to the stimulation frequency. The EEG signal is first decomposed into 5 sub-bands with different frequency bands using a standard filter bank. Then, a standard canonical correlation analysis is performed on each sub-band, and each sub-band will produce a set of correlation coefficients: k=1,2,…,40,n=1,2,…,5. k is the number of the stimulus target, and n is the number of the sub-band.

[0077] Each group of correlation coefficients is L1 normalized. Since all correlation coefficients ρ are positive, the absolute value can be ignored, and we can get: k=1,2,…,40, n=1,2,…,5.

[0078] Calculate credibility parameters n=1,2,…,5.

[0079] Different weight coefficients are assigned to different subbands. Since the signal-to-noise ratio of SSVEP decreases with the increase of harmonic order, the weight coefficient w(n) is defined as w(n)=n -1.25 +0.25,n=1,2,…,5.

[0080] use Fine-tune the weight coefficient to obtain an adaptive weight coefficient n=1,2,…,5.

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

[0082] The subband correlation coefficient can be obtained by weighted square summation of all subband correlation coefficients k=1,2,…,40.

[0083] Finally, the target with the largest sub-band correlation coefficient is selected as the recognition result, which corresponds to the most likely stimulus frequency.

[0084]

[0085] The protection scope of the SSVEP EEG signal recognition method described in the embodiment of the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0086] An embodiment of the present invention also provides an SSVEP EEG signal recognition system, which can implement the SSVEP EEG signal recognition method described in the present invention. However, the implementation device of the SSVEP EEG signal recognition system described in the present invention includes but is not limited to the structure of the SSVEP EEG signal recognition system listed in this embodiment. All structural deformations and replacements of the prior art made according to the principles of the present invention are included in the protection scope of the present 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 credibility 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 electroencephalogram signals.

[0089] The preprocessing module 42 is connected to the acquisition module 41 and is used to preprocess the SSVEP EEG signal to acquire 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 for each stimulation target based on the filter bank canonical correlation analysis algorithm. Wherein k = 1, 2, ..., K, n = 1, 2, ..., N, k is the serial number of the stimulus target, K is the number of stimulus targets, n is the serial number of the subband, 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 coefficients. Perform normalization processing to obtain the normalized correlation coefficient

[0092] The credibility module 45 is connected to the normalization module 44 and is used to calculate the credibility parameters corresponding to each sub-band of the effective SSVEP EEG signal. Among them, 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 convert the credibility parameter γ n Map to the preset interval and obtain the mapping credibility parameters

[0094] The setting module 47 is used to set the weight 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 map the credibility parameters based on the Fine-tune the weight coefficient w(n) to obtain an adaptive weight coefficient

[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 stimulation target. The stimulus target corresponding to the maximum value of the sub-band correlation coefficient is selected as the recognition result.

[0097] Among them, the structures and principles of the acquisition module 41, the preprocessing module 42, the correlation analysis module 43, the normalization module 44, the credibility module 45, the mapping module 46, the setting module 47, the adaptation module 48 and the recognition module 49 correspond one by one to the steps in the above-mentioned SSVEP EEG signal recognition method, so they will not be repeated here.

[0098] In the several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0099] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention. 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 of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0101] The embodiment of the present invention also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the SSVEP EEG signal recognition method of the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0102] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory.

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

[0104] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[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 executes the above-mentioned SSVEP electroencephalogram signal recognition method.

[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 gates or transistor logic devices, discrete hardware components.

[0107] like Figure 5As shown, the electronic device of the present invention is in the form of a general 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 types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

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

[0110] The 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, the storage system 523 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called 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, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 53 via one or more data medium interfaces. The memory 52 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various 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 the memory 52, such program modules 5241 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 thereof may include an implementation of a network environment. The program modules 5241 generally perform the functions and / or methods of the embodiments described herein.

[0112] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, 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 cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 54. Furthermore, the electronic device 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 network adapter 55. Figure 5 As shown, the network adapter 55 communicates with other modules of the electronic device via the 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 above-mentioned electronic device.

[0114] The EEG signal acquisition device is used to acquire EEG signals and send the EEG signals to the electronic device, wherein the EEG signal acquisition device and the electronic device perform signal transmission via wired or wireless means.

[0115] In summary, the SSVEP EEG signal recognition method and system, storage medium and electronic device of the present invention obtain the energy weights of EEG signals in different frequencies by collecting EEG data of the subject in the focused and non-focused states, thereby training to obtain an SSVEP EEG signal recognition model, and using the SSVEP EEG signal recognition model to realize the SSVEP EEG signal recognition of the subject; taking into account the effect of the energy of EEG signals of different frequencies in the frequency band on the concentration level, improving the accuracy of concentration monitoring; taking into account the differences in SSVEP EEG signal recognition models between people, improving the availability of SSVEP EEG signal recognition. Therefore, the present invention effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0116] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may 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 a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A SSVEP electroencephalogram signal recognition method, characterized in that: The method comprises the following steps: Obtain SSVEP EEG signals; Preprocessing the SSVEP electroencephalogram signal to obtain a valid SSVEP electroencephalogram signal; Based on the filter bank canonical correlation analysis algorithm, the correlation coefficients of each sub-band of the effective SSVEP EEG signal for each stimulation target are extracted. Where k = 1, 2, ..., K, n = 1, 2, ..., N, k is the number of stimulus targets, K is the number of stimulus targets, n is the number of sub-bands, N is the number of sub-bands, n = 1, 2, ..., N; The correlation coefficient Perform normalization processing to obtain the normalized correlation coefficient Calculate the credibility parameters corresponding to each sub-band of the effective SSVEP EEG signal Among them, max is the maximum value and 2ndmax is the second largest value; The credibility parameter γ n Map to the preset interval and obtain the mapping credibility parameters For different subbands, set the weight coefficient w(n)=n -1.25 +0.25; Based on the mapping credibility parameter Fine-tune the weight coefficient w(n) to obtain an adaptive weight coefficient Calculate the sub-band correlation coefficient of the SSVEP EEG signal for each stimulation target The stimulus target corresponding to the maximum value of the sub-band correlation coefficient is selected as the recognition result.

2. The SSVEP electroencephalogram signal recognition method according to claim 1, characterized in that: Acquiring SSVEP EEG signals includes the following steps: In the display target stimulus paradigm; The SSVEP electroencephalogram (EEG) signal generated when the subject watches the target stimulation paradigm is collected based on the EEG acquisition device.

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

4. The SSVEP electroencephalogram signal recognition method according to claim 1, characterized in that: Extracting the correlation coefficients of each sub-band of the effective SSVEP electroencephalogram signal for each stimulation target comprises the following steps: Decomposing the effective SSVEP electroencephalogram signal into a plurality of sub-bands by using a filter bank based on a filter bank canonical correlation analysis algorithm; For each sub-band, a canonical correlation analysis is performed for each stimulus target to obtain the corresponding correlation coefficient.

5. The SSVEP electroencephalogram signal recognition method according to claim 1, characterized in that: according to The normalized correlation coefficient is obtained.

6. The SSVEP electroencephalogram signal recognition method according to claim 1, characterized in that: according to Get the mapping credibility parameter Where [a,b] is the preset interval, γ max and γ min Represent the credibility parameter γ n The maximum and minimum values ​​in .

7. The SSVEP electroencephalogram signal recognition method according to claim 1, characterized in that: according to Get the adaptive weight coefficient.

8. A SSVEP electroencephalogram 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 a recognition module; The acquisition module is used to acquire SSVEP electroencephalogram 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 electroencephalogram signal for each stimulation target based on the filter bank canonical correlation analysis algorithm. Where k = 1, 2, ..., K, n = 1, 2, ..., N, k is the number of stimulus targets, K is the number of stimulus targets, n is the number of sub-bands, N is the number of sub-bands, n = 1, 2, ..., N; The normalization module is used to normalize the correlation coefficient Perform normalization processing to obtain the normalized correlation coefficient The credibility module is used to calculate the credibility parameters corresponding to each sub-band of the valid SSVEP EEG signal Among them, max is the maximum value and 2ndmax is the second largest value; The mapping module is used to transform the credibility parameter γ n Map to the preset interval and obtain the mapping credibility parameters The setting module is used to set the weight coefficient w(n)=n for different sub-bands. -1.25 +0.25; The adaptive module is used to map the credibility parameters based on the Fine-tune the weight coefficient w(n) to obtain an adaptive weight coefficient The recognition module is used to calculate the sub-band correlation coefficient of the SSVEP electroencephalogram signal for each stimulation target The stimulus target corresponding to the maximum value of the sub-band 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 a processor, the SSVEP electroencephalogram signal recognition method according to any one of claims 1 to 7 is implemented.

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 so that the electronic device executes the SSVEP electroencephalogram signal recognition method according to any one of claims 1 to 7.

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