Partition backlight brain-computer interaction intelligent display device and interaction method thereof

Abstract

The invention provides partitioned backlight brain-computer interaction intelligent display equipment and an interaction method thereof. The intelligent display equipment comprises a display module, an electroencephalogram signal acquisition module and a central processing module, wherein the display module comprises a partition backlight signal generation module, a partition backlight control module, a storage module, a partition backlight module and a liquid crystal display module; the liquid crystal display module is connected with the central processing module, and the electroencephalogram signal acquisition module is connected with the central processing module; the central processing module comprises an interactive interface generation module, an electroencephalogram signal analysis module and a communication module; by collecting and analyzing the electroencephalogram signals in real time, the operation of the display device can be dynamically adjusted according to the electroencephalogram response of the user, and it is ensured that the interaction process is more natural, intelligent and efficient. The method not only improves the accuracy and reliability of brain-computer interaction, but also has relatively high practicability and a wide market prospect.

Description

Technical Field

[0001] The present invention relates to the fields of human-computer interaction, artificial intelligence, brain-computer interface, display science and technology, and in particular to a partitioned backlight brain-computer interaction intelligent display device and an interaction method thereof. Background Art

[0002] Brain-computer interface (BCI) technology involves the cross-integration of multiple disciplines. Among them, the brain-computer interface based on steady-state visual evoked potential (SSVEP-BCI) uses the human brain's specific response to specific visual frequencies, has the characteristics of directness and naturalness, and has great potential in future brain-computer interaction applications.

[0003] Although SSVEP-BCI technology has made significant progress, the current steady-state visual stimulation system (LCD-SSVEP) based on traditional liquid crystal display (LCD) still faces some technical bottlenecks and challenges.

[0004] (1) The quality of LCD stimulation signals is poor. The low refresh rate of the LCD causes distortion in the visual stimulation waveform, making it difficult to stimulate high-quality SSVEP EEG signals, thus limiting the practical application of the SSVEP-BCI system.

[0005] (2) LCDs are prone to visual fatigue. "LCDs are prone to visual fatigue. Long-term use may cause users to feel uncomfortable and lose attention.

[0006] (3) LCD is expensive. The LCD-SSVEP system usually requires a high refresh rate LCD display, but the structure of this type of display is relatively complex and expensive. Summary of the invention

[0007] Technical problem: In view of the above-mentioned defects or deficiencies in the prior art, the present invention proposes a liquid crystal display SSVEP technology (PB-SSVEP) that uses high refresh rate partitioned backlight to generate visual stimulation, and based on the PB-SSVEP technology, proposes a partitioned backlight brain-computer interaction intelligent display device and its interaction method. The partitioned backlight TV cooperates with the interactive interface to generate a function menu with visual stimulation. The EEG data is collected in real time using an EEG acquisition device, and then the frequency components in the EEG data are analyzed using the correlation analysis method to obtain the recognition result. According to the recognition result, it is fed back to the intelligent display device, and the intelligent display device can complete the corresponding operation instructions according to the feedback, thereby realizing the interactive process of converting the user's intention into the actual behavior of the intelligent display device.

[0008] Technical solution: A partitioned backlight brain-computer interaction intelligent display device of the present invention comprises a display module, an electroencephalogram signal acquisition module, and a central processing module; wherein the display module comprises a partitioned backlight signal generation module, a partitioned backlight control module, a storage module, a partitioned backlight module, and a liquid crystal display module; wherein the partitioned backlight signal generation module is connected to the central processing module, and is used to generate a visual stimulation signal and transmit it to the partitioned backlight control module; the partitioned backlight control module controls the brightness and flickering of the partitioned backlight module through an LED driving module; the storage module comprises a power-off storage memory and a volatile memory for storing partition information and programs; the liquid crystal display module is connected to the central processing module, and is used to display an interactive interface and update the display content; The EEG signal acquisition module is worn on the user's pillow and connected to the central processing module to collect the user's EEG signals in real time and transmit them to the central processing module; The central processing module includes an interactive interface generation module, an EEG signal analysis module and a communication module; wherein the interactive interface generation module is connected to the display module to generate an interactive interface and control the display target; the EEG signal analysis module is connected to the EEG signal acquisition module to process the acquired EEG signals, output feature data and perform classification and recognition; the communication module is connected to the display module and the EEG signal analysis module to transmit interactive data and feedback recognition results.

[0009] The EEG signal acquisition module is a non-invasive EEG acquisition device, which uses eight-channel dry electrodes or semi-dry electrodes.

[0010] The partition backlight signal generation module includes a WiFi / Bluetooth module, an HDMI interface module, a partition brightness feature extraction module and a backlight information processing module; the partition backlight signal generation module is respectively connected to the partition backlight control module, the storage module and the liquid crystal display module.

[0011] The partition backlight control module includes 2 or more LED driving modules, each module includes 2 or more LED partitions; the partition backlight control module is respectively connected to the partition backlight signal generating module, the storage module, and the partition backlight module.

[0012] The power-off saving memory in the storage module is Flash memory, EPROM, eMMC, UFS, volatile memory, solid state drive or mechanical hard disk, wherein the volatile memory is static RAM and dynamic RAM, and the storage module includes power-off saving memory and volatile memory.

[0013] The light source of the partition backlight module is an LED backlight source, or a quantum dot QLED.

[0014] The central processing module is a general-purpose computer processor or a computer device or embedded device based on large-scale programmable logic.

[0015] The EEG signal analysis module includes a signal preprocessing module, a feature extraction module, and a classification and recognition module, wherein the EEG signal is connected to the input of the signal preprocessing module, the output of the signal preprocessing module is connected to the input of the feature extraction module, and the output of the feature extraction module is connected to the input of the classification and recognition module; the classification and recognition module performs decoding and recognition based on correlation analysis method CCA or filter bank correlation analysis method FBCCA.

[0016] The communication module includes an interface display communication module, a stimulation information communication module, and a recognition result communication module, and the communication modules are respectively connected to the interactive interface generation module and the display module.

[0017] The interactive method of the partitioned backlight brain-computer interactive intelligent display device of the present invention comprises the following steps: Step 1: Power on and start the system. The backlight information processing module in the display module reads the program from the power-off storage memory and stores it in the volatile memory, and reads the frequency and phase information of each partition from the power-off storage memory and stores it in the partition backlight module; the central processing module modifies the frequency and phase information of each partition through the stimulation information communication module, and the display module will synchronously update the modified information to the power-off storage memory and the partition backlight module; the central processing module transmits the target block size, position, color and other information of the interactive interface to the liquid crystal display module of the display module through the interface display communication module. After the central processing module confirms that the lead status of the EEG acquisition device is normal, it sends a startup command to start the entire intelligent display device; Step 2: generating partition backlight signals. The partition backlight signal generating module calculates and generates the brightness value at each moment required for the corresponding frequency stimulation according to the frequency phase information of each partition. The calculation result is transmitted to the LED driver chip of the partition backlight control module to control the corresponding partition to flash. Each partition flashes periodically with a flashing frequency of 18-35Hz. At the same time, the liquid crystal display module displays the target corresponding to the interactive interface on the liquid crystal panel. Step 3: The user interacts with the interactive interface. The user watches the display content of the intelligent display device and interacts with the interactive interface. The EEG signal acquisition module collects the user's EEG data in real time and transmits it to the EEG signal analysis module in the central processing module; Step 4: Signal preprocessing. After receiving the EEG signal, the signal preprocessing module first unpacks the data, unpacks the original EEG signal in the data format of packet sequence input into EEG data in the shape of 8 channels, and stores the unpacked data in a first-in-first-out buffer area; the data in the buffer area is processed to remove baseline drift and perform bandpass filtering to filter out frequency bands that are not of interest, and filter out electrical oscillation frequencies; the bandpass cutoff frequency of the bandpass filter is 6Hz~90Hz, the filtered electrical oscillation frequencies include 25Hz and 50Hz, and the notch width is 1Hz~3Hz; The packet sequence means that the data format of the original data collected by the EEG signal acquisition module and transmitted to the EEG signal analysis module is in the form of a packet sequence, which needs to be unpacked into an 8-channel usable data format before subsequent filtering and feature extraction; Step 5: Feature extraction. The feature extraction module takes out the preprocessed EEG signal from the buffer area, generates the corresponding base frequency and frequency multiple template signals according to the target frequency list, and uses sine and cosine functions to generate the reference signal matrix of the target frequency and its frequency multiples; then, the EEG signal and the template signal are calculated by correlation analysis method CCA or filter bank correlation analysis method FBCCA, and the frequency domain response characteristics corresponding to each target frequency are extracted. Finally, the calculated feature values ​​are used as the input of the classification and recognition module; Step 6: Classification and recognition. The classification and recognition module calculates the correlation between the EEG signal and the template signal according to the frequency domain response characteristics output by the feature extraction module to obtain the correlation coefficient. If the correlation coefficient reaches the set threshold and the recognition is consistent for three consecutive times, the recognition result is output and the classification status is updated. If the recognition results are different for three consecutive times, the number of recognitions is reset to 1, and the stored classification result is updated with the current recognition result. If the correlation coefficient does not reach the set correlation threshold, the number of recognitions is set to 0, and the stored classification result is updated to the unrecognized state. Step 7: Feedback of recognition results: the central processing module feeds back the recognition results output by the classification recognition module to the display module through the recognition result communication module; the display module echoes the target according to the recognition result and performs corresponding behavioral operations; when the recognition result is fed back, if the state of the EEG signal acquisition module is abnormal, the user is reminded through the sound and light warning method, and the fault information is saved; Step 8: Repeat steps 3 to 7 to achieve continuous interaction.

[0018] Beneficial effects: Compared with the prior art, the advantages of the present invention are as follows: The partitioned backlight brain-computer interaction intelligent display device and its interaction method of the present invention have significant advantages over the prior art. By combining the partitioned backlight technology with the steady-state visual evoked potential (SSVEP), the shortcomings of traditional liquid crystal displays (LCDs) in terms of refresh rate and visual fatigue are solved, the quality of visual stimulation is improved, and the recognition accuracy and stability of EEG signals are improved. The high refresh rate partitioned backlight module effectively reduces visual fatigue. By real-time acquisition and analysis of EEG signals, the present invention can dynamically adjust the operation of the display device according to the user's EEG response, ensuring that the interaction process is more natural, intelligent and efficient. The present invention not only improves the accuracy and reliability of brain-computer interaction, but also has strong practicality and broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 FIG. 1 is a schematic diagram of the system structure of the present invention in one embodiment.

[0020] Figure 2 FIG. 1 is a schematic diagram of a display module structure and a working method thereof according to an embodiment of the present invention.

[0021] Figure 3 FIG. 1 is a schematic diagram of the EEG signal preprocessing process of the present invention in one embodiment.

[0022] Figure 4 FIG. 1 is a schematic diagram of the EEG signal feature extraction process of the present invention in one embodiment.

[0023] Figure 5 FIG. 1 is a schematic diagram of the EEG signal classification and recognition process of the present invention in one embodiment.

[0024] The figure includes: display module 1, partition backlight signal generation module 11, partition backlight control module 12, storage module 13, partition backlight module 14, liquid crystal display module 15, WiFi / Bluetooth module 111, HDMI interface module 112, partition brightness feature extraction module 113, backlight information processing module 114; EEG signal acquisition module 2; Central processing module 3, interactive interface generation module 31, EEG signal analysis module 32, communication module 33, signal preprocessing module 321, feature extraction module 322, classification and recognition module 323, interface display communication module 331, stimulus information communication module 332, recognition result communication module 333. DETAILED DESCRIPTION

[0025] The following is a description of the specific embodiments in conjunction with the accompanying drawings. Specific exemplary embodiments in which the present invention may be implemented are described by way of example. These embodiments are described in sufficient detail to enable those skilled in the art to practice the concepts disclosed herein, and it should be understood that the various disclosed embodiments may be modified and other embodiments may be utilized without departing from the scope of the present disclosure. Therefore, the following detailed description is not to be considered restrictive.

[0026] The present invention aims to provide a partitioned backlight brain-computer interaction intelligent display device and its interaction method, which combines the steady-state visual evoked potential (SSVEP) technology to collect and analyze EEG signals in real time, identify the user's interaction intention, and feed back the corresponding recognition results to the display device for corresponding operations, thereby improving the naturalness, intelligence and efficiency of brain-computer interaction.

[0027] To achieve the above functions, the present invention adopts the following examples: Example 1: Figure 1 A partitioned backlight brain-computer interaction intelligent display device shown includes a display module 1, an EEG signal acquisition module 2, and a central processing module 3. The display module 1 includes a partitioned backlight signal generation module 11, a partitioned backlight control module 12, a storage module 13, a partitioned backlight module 14, and a liquid crystal display module 15; wherein the partitioned backlight signal generation module 11 is connected to the central processing module 3, and is used to generate a visual stimulation signal and transmit it to the partitioned backlight control module 12; the partitioned backlight control module 12 controls the brightness and flickering of the partitioned backlight module 14 through the LED driving module; the storage module 13 includes a power-off storage memory and a volatile memory, and is used to store partition information and programs; the liquid crystal display module 15 is connected to the central processing module 3, and is used to display an interactive interface and update the display content; the EEG signal acquisition module 2 is a non-invasive The device uses an eight-channel dry electrode or semi-dry electrode, which is worn on the user's pillow and connected to the central processing module 3, and is used to collect the user's EEG signals in real time and transmit them to the central processing module 3; the central processing module 3 includes an interactive interface generation module 31, an EEG signal analysis module 32 and a communication module 33; wherein the interactive interface generation module 31 is connected to the display module 1, and is used to generate an interactive interface and control the display target; the EEG signal analysis module 32 is connected to the EEG signal acquisition module 2, and is used to process the collected EEG signals, output feature data and perform classification and recognition; the communication module 33 is connected to the display module 1 and the EEG signal analysis module 32, and is used to transmit interactive data and feedback recognition results.

[0028] The light source of the partition backlight module 14 is an LED backlight source or a quantum dot (QLED); the classification and recognition module 323 of the EEG signal analysis module 32 performs decoding and recognition based on a correlation analysis method (CCA) or a filter bank correlation analysis method (FBCCA); the central processing module 3 is a general-purpose computer processor based on an X86 complex instruction set or its Atom reduced instruction set, an ARM, MIPS, or PowerPC architecture based on a reduced instruction set RISC, or a computer device or embedded device based on large-scale programmable logic; the power-off storage memory in the storage module 13 is a flash memory, an EPROM, an eMMC, a USF, a solid-state hard disk, or a mechanical hard disk; the volatile memory is a static RAM (SRAM) and a dynamic RAM (DRAM); The partition backlight signal generation module 11 includes a WiFi / Bluetooth module 111, an HDMI interface module 112, a partition brightness feature extraction module 113 and a backlight information processing module 114. The storage module 13 includes a power-off storage memory and a volatile memory. The partition backlight control module 12 includes 2 or more LED driving modules, each of which includes 2 or more LED partitions. The EEG signal analysis module 32 includes a signal preprocessing module 321 for removing baseline drift and filtering, a feature extraction module 322 for extracting signal features, and a classification recognition module 323 for decoding and recognition; the communication module 33 includes an interface display communication module 331, a stimulation information communication module 332 and a recognition result communication module 333.

[0029] The interactive method of the partitioned backlight brain-computer interactive intelligent display device comprises the following steps: Step 1: The system is powered on and started. The backlight information processing module 114 of the display module reads the program from the power-off storage memory and stores it in the volatile memory. The frequency and phase information of each partition is read from the power-off storage memory and stored in the partition backlight module 14. The central processing module 3 modifies the frequency and phase information of each partition through the stimulation information communication module 332, and the display module synchronously updates the information to the power-off storage memory and the backlight partition information library. The central processing module 3 transmits the target block size, position, color and other information of the interactive interface to the liquid crystal display module 15 through the interface display communication module 331. After confirming that the lead status of the EEG acquisition device is normal, the central processing module 3 sends a start command to start the entire intelligent display device.

[0030] Step 2: Generate partition backlight signal, such as Figure 2As shown, the partition backlight signal generation module 11 calculates the brightness value at each moment according to the frequency and phase information in the backlight partition information library, and transmits the calculation result to the LED driver chip of the partition backlight control module 12 to control the corresponding partition to flash. The liquid crystal display module 15 displays the target content on the interactive interface on the liquid crystal panel according to the information in the interface target information library.

[0031] Step 3: The user interacts with the interactive interface, the user watches the display content of the intelligent display device and interacts with the interactive interface. The 8-channel EEG signal acquisition module 2 collects the user's EEG data in real time and transmits the data to the EEG signal analysis module 32 in the central processing module 3.

[0032] Step 4: If Figure 3 As shown, after receiving the EEG signal, the signal preprocessing module 321 first decodes the data, converts the data format of different packet sequences into EEG data in the shape of 8 channels, and stores them in a first-in-first-out buffer. Subsequently, the data in the buffer is subjected to baseline drift removal processing, and a bandpass filter is used to filter out non-concern frequency bands and electrical oscillation frequencies.

[0033] Step 5: If Figure 4 As shown, the feature extraction module 322 extracts the preprocessed EEG signal from the buffer area, generates the corresponding base frequency and frequency multiple template signals according to the target frequency list, and generates the reference signal matrix of the target frequency and its frequency multiples using sine and cosine functions. Through a specific feature extraction algorithm, the EEG signal and the template signal are calculated to extract the frequency domain response characteristics of each target frequency, and the extracted feature values ​​are used as the input of the classification recognition module 323.

[0034] Step 6: If Figure 5 As shown, the classification recognition module 323 obtains the correlation coefficient by calculating the correlation between the EEG signal and the template signal according to the frequency domain response characteristics output by the feature extraction module 322. If the correlation coefficient reaches the set threshold and the recognition is consistent for three consecutive times, the recognition result is output and the classification state is updated; if the recognition results are inconsistent for three consecutive times, the number of recognitions is reset to 1, and the stored classification result is updated with the current recognition result; if the correlation coefficient does not reach the set correlation threshold, the number of recognitions is set to 0, and the stored classification result is updated to the unrecognized state.

[0035] Step 7: The central processing module 3 feeds back the recognition result output by the classification recognition module 323 to the liquid crystal display module 15 through the recognition result communication module 333. The liquid crystal display module 15 displays the target according to the recognition result and performs corresponding behavior operations.

[0036] Step 8: Continue user interaction by repeating steps 3 to 7 to achieve continuous feedback and control of brain-computer interaction.

[0037] Example 2: In the partitioned backlight brain-computer interaction intelligent display device described in Example 1, each partition in step 1 and step 2 flashes periodically, and the flashing frequency is 18~35Hz.

[0038] Example 3: In the partitioned backlight brain-computer interaction intelligent display device described in Example 1, the passband cutoff frequency of the filter in step 4 is 6Hz~90Hz, the filtered electrical oscillation frequencies include 25Hz and 50Hz, and the notch width is 1Hz~3Hz.

[0039] Example 4: For the partitioned backlight brain-computer interaction intelligent display device described in Example 1, when the recognition result is fed back in step seven, if the state of the EEG signal acquisition module 2 is abnormal, the user is reminded through an audible and visual warning method, and the fault information is saved.

[0040] Example 5: In the partitioned backlight brain-computer interaction intelligent display device described in Example 1, the feature extraction algorithm in step five is correlation analysis (CCA) or filter bank correlation analysis (FBCCA).

Claims

1. A partitioned backlight brain-computer interaction intelligent display device, characterized in that: The device comprises a display module (1), an electroencephalogram signal acquisition module (2), and a central processing module (3); wherein the display module (1) comprises a partition backlight signal generation module (11), a partition backlight control module (12), a storage module (13), a partition backlight module (14), and a liquid crystal display module (15); wherein the partition backlight signal generation module (11) is connected to the central processing module (3) and is used to generate a visual stimulation signal and transmit it to the partition backlight control module (12); the partition backlight control module (12) controls the brightness and flickering of the partition backlight module (14) through an LED driving module; the storage module (13) comprises a power-off storage memory and a volatile memory for storing partition information and programs; and the liquid crystal display module (15) is connected to the central processing module (3) and is used to display an interactive interface and update display content; The EEG signal acquisition module (2) is worn on the user's pillow and connected to the central processing module (3) for real-time acquisition of the user's EEG signals and transmission to the central processing module (3); The central processing module (3) comprises an interactive interface generation module (31), an electroencephalogram signal analysis module (32) and a communication module (33); wherein the interactive interface generation module (31) is connected to the display module (1) and is used to generate an interactive interface and control a display target; the electroencephalogram signal analysis module (32) is connected to the electroencephalogram signal acquisition module (2) and is used to process the acquired electroencephalogram signals, output feature data and perform classification and recognition; and the communication module (33) is connected to the display module (1) and the electroencephalogram signal analysis module (32) and is used to transmit interactive data and feedback recognition results.

2. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The EEG signal acquisition module (2) is a non-invasive EEG acquisition device, which uses eight-channel dry electrodes or semi-dry electrodes.

3. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The partitioned backlight signal generation module (11) comprises a WiFi / Bluetooth module (111), an HDMI interface module (112), a partitioned brightness feature extraction module (113) and a backlight information processing module (114); the partitioned backlight signal generation module (11) is respectively connected to the partitioned backlight control module (12), the storage module (13) and the liquid crystal display module (15).

4. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The partitioned backlight control module (12) comprises two or more LED driving modules, each module comprising two or more LED partitions; the partitioned backlight control module (12) is respectively connected to the partitioned backlight signal generating module (11), the storage module (13), and the partitioned backlight module (14).

5. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The power-off saving memory in the storage module (13) is a Flash memory, an EPROM, an eMMC, a UFS, a volatile memory, a solid state hard disk or a mechanical hard disk, wherein the volatile memory is a static RAM and a dynamic RAM, and the storage module (13) includes a power-off saving memory and a volatile memory.

6. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The light source of the partitioned backlight module (14) is an LED backlight source, or a quantum dot QLED.

7. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The central processing module (3) is a general-purpose computer processor or a computer device or embedded device based on large-scale programmable logic.

8. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The electroencephalogram signal analysis module (32) comprises a signal preprocessing module (321), a feature extraction module (322), and a classification and recognition module (323), wherein the electroencephalogram signal is connected to the input of the signal preprocessing module (321), the output of the signal preprocessing module (321) is connected to the input of the feature extraction module (322), and the output of the feature extraction module (322) is connected to the input of the classification and recognition module (323); the classification and recognition module (323) performs decoding and recognition based on a correlation analysis method CCA or a filter bank correlation analysis method FBCCA.

9. The partitioned backlight brain-computer interaction intelligent display device according to claim 1 is characterized in that: The communication module (33) comprises an interface display communication module (331), a stimulation information communication module (332), and a recognition result communication module (333), and the communication module (33) is respectively connected to the interactive interface generation module (31) and the display module (1).

10. An interaction method based on the partitioned backlight brain-computer interaction intelligent display device according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: The system is powered on and started. The backlight information processing module (114) in the display module reads the program from the power-off storage memory and stores it in the volatile memory, and reads the frequency and phase information of each partition from the power-off storage memory and stores it in the partition backlight module (14); the central processing module (3) modifies the frequency and phase information of each partition through the stimulation information communication module (332), and the display module (1) synchronously updates the modified information to the power-off storage memory and the partition backlight module (14); the central processing module (3) transmits the target block size, position, color and other information of the interactive interface to the liquid crystal display module (15) of the display module through the interface display communication module (331). After the central processing module confirms that the lead status of the electroencephalogram acquisition device is normal, it sends a startup instruction to start the entire intelligent display device; Step 2: generating a partition backlight signal. The partition backlight signal generating module (11) calculates and generates the brightness value required for the corresponding frequency stimulation at each moment according to the frequency phase information of each partition. The calculation result is transmitted to the LED driver chip of the partition backlight control module (12) to control the corresponding partition to flash. Each partition flashes periodically with a flashing frequency of 18 to 35 Hz. At the same time, the liquid crystal display module (15) displays the target corresponding to the interactive interface on the liquid crystal panel. Step 3: The user interacts with the interactive interface. The user views the display content of the intelligent display device and interacts with the interactive interface. The EEG signal acquisition module (2) acquires the user's EEG data in real time and transmits it to the EEG signal analysis module (32) in the central processing module. Step 4: signal preprocessing. After receiving the EEG signal, the signal preprocessing module (321) first unpacks the data, unpacks the original EEG signal in the data format of the packet sequence input into EEG data in the shape of 8 channels, and stores the unpacked data into a first-in-first-out buffer area; The data in the buffer area is processed to remove baseline drift and perform bandpass filtering to filter out frequency bands of no concern and filter out electrical oscillation frequencies; the bandpass cutoff frequency of the bandpass filter is 6Hz~90Hz, the filtered electrical oscillation frequencies include 25Hz and 50Hz, and the notch width is 1Hz~3Hz; The packet sequence means that the data format of the original data collected by the EEG signal acquisition module and transmitted to the EEG signal analysis module is in the form of a packet sequence, which needs to be unpacked into an 8-channel usable data format before subsequent filtering and feature extraction; Step 5: Feature extraction, the feature extraction module (322) takes out the preprocessed EEG signal from the buffer area, generates the corresponding base frequency and frequency multiple template signals according to the target frequency list, and generates the reference signal matrix of the target frequency and its frequency multiples using sine and cosine functions; then, the EEG signal and the template signal are calculated by correlation analysis method CCA or filter bank correlation analysis method FBCCA, and the frequency domain response characteristics corresponding to each target frequency are extracted, and finally the calculated characteristic values ​​are used as the input of the classification recognition module (323); Step 6: Classification and recognition: the classification and recognition module (323) obtains the correlation coefficient by calculating the correlation between the EEG signal and the template signal according to the frequency domain response characteristics output by the feature extraction module; if the correlation coefficient reaches the set threshold and the recognition is consistent for three consecutive times, the recognition result is output and the classification status is updated; If the recognition results are different for three consecutive times, the number of recognitions is reset to 1, and the stored classification result is updated with the current recognition result; If the correlation coefficient does not reach the set correlation threshold, the number of identified times is set to 0, and the stored classification result is updated to an unidentified state; Step 7: Feedback of recognition results: the central processing module (3) feeds back the recognition results output by the classification recognition module to the liquid crystal display module (15) through the recognition result communication module (333); the display module echoes back the target according to the recognition result and performs corresponding behavioral operations; when the recognition result is fed back, if the state of the electroencephalogram signal acquisition module (2) is abnormal, the user is reminded by an audible and visual warning method, and the fault information is saved; Step 8: Repeat steps 3 to 7 to achieve continuous interaction.

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