A biofeedback system for electroencephalogram, electromyography and pulse wave and its analysis method

Abstract

The present invention relates to an electroencephalogram (EEG), electromyography (EMG), and pulse wave biofeedback system and analysis method thereof. The system comprises a signal collector, a signal receiver, and biofeedback instrument software. The signal collector is configured to collect EEG, EMG, and pulse wave signals; the signal receiver is configured to receive biosignal data from the signal collector and forward the received data to the biofeedback instrument software on a PC, where the biofeedback instrument software processes and analyzes the data. Finally, different state indices are obtained through analysis of the EEG, EMG, and pulse wave biosignals: a brain relaxation index, a body relaxation index, and a psychological relaxation index. The system analyzes the EEG, EMG, and pulse wave biosignals to obtain different state indices: a brain relaxation index, a body relaxation index, and a psychological relaxation index. The system then uses the feedback system's continuous positive guidance to gradually improve patients' anxiety.

Description

Technical Field

[0001] The present invention relates to the technical field of biological signal acquisition, and in particular to a biofeedback system for electroencephalogram, electromyography and pulse wave and an analysis method thereof. Background Art

[0002] With the increasing pressures of work and life, anxiety disorders are becoming an increasingly common mental illness in today's society. Symptoms include restlessness, nervousness, rapid breathing, increased heart rate, weakness or fatigue, difficulty sleeping, and decreased concentration. Approximately 12% of the world's population suffers from anxiety disorders, most often in adolescents or middle-aged adults.

[0003] Currently, the main treatments for anxiety disorders are medication and psychotherapy. While effective, anti-anxiety medications are often addictive, temporary, and have numerous side effects, such as dizziness and vertigo. Psychological therapy is limited by the therapist's medical expertise and the patient's level of cooperation, resulting in individualized effectiveness.

[0004] With the development of biofeedback theories and technologies such as EEG, EMG, and pulse waves, biofeedback devices for treating anxiety disorders have begun to appear on the market. However, these devices only have a single EEG or EMG acquisition function, which provides incomplete biofeedback information and cannot fully assess the patient's condition. Among the symptoms of anxiety, shortness of breath and increased heart rate can be analyzed through pulse wave analysis to understand the patient's condition, while tension and restlessness can be analyzed through EMG analysis. Decreased concentration, weakness, or fatigue can be analyzed through EEG analysis. Therefore, a single acquisition function cannot meet the needs of a comprehensive assessment and analysis of anxiety disorders. Summary of the Invention

[0005] The purpose of the present invention is to provide an electroencephalogram, electromyography and pulse wave biofeedback system and its analysis method, which obtains different state indices by analyzing three biological signals: brain relaxation index, body relaxation index and psychological relaxation index. Then, through continuous positive guidance of the feedback system, the patient can gradually improve his or her anxiety state, so as to solve the problems encountered in the above-mentioned background technology.

[0006] To achieve the above object, the technical solution of the present invention is as follows:

[0007] A biofeedback system for electroencephalogram (EEG), electromyography (EMG), and pulse waves and an analysis method thereof include a signal collector, a signal receiver, and biofeedback instrument software. The signal collector is used to collect EEG, EMG, and pulse wave signals; the signal receiver is used to receive biosignal data from the signal collector and forward the received data to the biofeedback instrument software on a PC, which processes and analyzes the data. Finally, different state indices are obtained through analysis of the EEG, EMG, and pulse wave biosignals: a brain relaxation index, a body relaxation index, and a psychological relaxation index.

[0008] In the above solution, the common mode rejection ratio of the biological signal acquisition circuit is greater than 100dB, the system noise is less than 2uVpp, the impedance is greater than 20MΩ, and the measurement range is 2μV to 5000μV.

[0009] In the above scheme, the signal collector includes a main controller, a Bluetooth cluster machine module, a voice circuit and a biological signal acquisition circuit. The Bluetooth cluster machine module and the biological signal acquisition circuit are respectively connected to the main controller. The biological signal acquisition circuit sends the collected EEG, EMG and pulse wave data to the main controller. The main controller sends the data to the signal receiver through the Bluetooth cluster machine module after pre-processing the data; the voice circuit is connected to the main controller, and the voice circuit plays voice according to the command of the biofeedback instrument software to guide the patient to adjust his state.

[0010] Furthermore, the biological signal acquisition circuit includes an EEG and EMG acquisition circuit and a pulse acquisition circuit composed of an analog front-end chip. The EEG and EMG acquisition circuit is used to collect the patient's EEG signals and EMG signals, and the pulse acquisition circuit is used to collect the patient's pulse wave signals. The EEG and EMG acquisition circuit is provided with an electrode detachment detection device.

[0011] In the above scheme, the software of the signal collector includes a collector control module, a command parsing module, a Bluetooth transceiver module, a sound module, an EEG reading module, an EMG reading module, a pulse reading module and a data processing module; the collector control module is connected to the data processing module, the collector control module is respectively connected to the command parsing module, the Bluetooth transceiver module and the sound module, and the data processing module is respectively connected to the EEG reading module, the EMG reading module and the pulse reading module; the Bluetooth transceiver module is responsible for sending and receiving Bluetooth data, the sound module is responsible for playing and stopping the sound, the command parsing module is responsible for parsing the Bluetooth data, the EEG, EMG and pulse reading modules are respectively responsible for reading the corresponding signals, and the data processing module is responsible for parsing and processing the EEG, EMG and pulse.

[0012] In the above scheme, the biofeedback instrument software includes a control module and an interaction module, the control module is connected to the interaction module, the control module is connected to the log module, display module, data receiving module, analysis and processing module, storage and playback module, and the interaction module is connected to the patient management module, video training module, and game training module; wherein, the analysis and processing module is used to process and analyze EEG, EMG and pulse wave data to generate a brain relaxation index L H , body relaxation index L B and psychological relaxation index L M .

[0013] A method for analyzing an electroencephalogram (EEG), electromyography (EMG), and pulse wave biofeedback system comprises: a signal collector collecting three signals of a patient: EEG, EMG, and pulse wave; a signal receiver receiving biosignal data from the signal collector and forwarding the received data to biofeedback instrument software on a PC, which processes and analyzes the data; and finally, different state indices are obtained through analysis of the three biosignals: a brain relaxation index, a body relaxation index, and a psychological relaxation index.

[0014] In the above solution, the acquisition process of the signal collector is as follows:

[0015] Step 1: The patient wears the terminal collection device, waits for the voice broadcast instruction, and starts playing the voice;

[0016] Step 2: The hardware of the signal collector is initialized and runs, waiting for the acquisition start command. If the acquisition command starts, all data of the analog front end are read. If the acquisition command does not start, it continues to wait for the acquisition start command;

[0017] Step 3: Analyze the EEG data and EMG data; in addition, the ADC reads the pulse wave value, combines the EEG data, EMG data, and pulse wave value data, and combines all the data into one frame of data;

[0018] Step 4: Send the combined data to the signal receiver via Bluetooth, and then wait for the end command issued by the receiving system. If the end command is not received, continue to wait for the acquisition start command.

[0019] In addition, in the above scheme, the collection process of the biofeedback instrument software is as follows:

[0020] Step 1: The system matches the patient, sends a connection instruction, connects to the signal collector, and waits for a successful connection. If the connection is successful, the acquisition command is sent. If not, the system continues to wait.

[0021] Step 2: Wait for data to be received, split the EEG data, EMG data, and pulse wave data, calculate the brain relaxation index, body relaxation index, and psychological relaxation index through algorithms, and display each index on the display screen;

[0022] In step 2, when processing EEG data, the EEG data is first filtered to remove irrelevant signals outside the effective frequency band, and then a fast Fourier transform is performed to obtain the amplitude P at n Hz. n :

[0023]

[0024] Among them, T e is the FFT processing period, EEG(t) is the amplitude at time t,

[0025] Define the frequency coefficient sequences Cn and Dn, and get the frequency energy ratio R as

[0026]

[0027] Among them, the maximum value of n is N, the value range of N is [1, Fs / 2], Fs is the signal sampling rate,

[0028] The normalized brain-related index L H for,

[0029]

[0030] Among them, Th is the threshold, and Cn, Dn, and Th are all obtained through deep learning based on the labeled EEG data and the corresponding EEG index data;

[0031] When processing electromyographic data, the real-time root mean square value of electromyography is calculated according to the following formula to obtain the body relaxation index L B :

[0032]

[0033] When processing the pulse wave, the pulse wave data is firstly subjected to a 0.5Hz-4.5Hz bandpass filter, that is, invalid signals other than 30bpm-245bpm are filtered out. Then, the filtered waveform is subjected to frequency domain analysis and time domain analysis. When performing frequency domain analysis, a fast Fourier transform (FFT) is first performed to obtain the frequency point with the highest amplitude F. H (Hz), heart rate HR=60*F H ;

[0034] Then the LF value expressing the overall sympathetic nerve activity and the HF value expressing the parasympathetic nerve activity were calculated.

[0035]

[0036]

[0037] If LF / HF is equal to 1, which is the best state, i.e. 100 points, and 3 is the worst state, i.e. 0 points, then the psychological relaxation index L M for:

[0038]

[0039] Here, x is the value of LF / HF.

[0040] Furthermore, the Th is 0.3, and the Cn and Dn are:

[0041]

[0042]

[0043] Step 3: By comparing with the standard index range, determine whether the displayed index value is too high. If it is too high, send a voice reminder to relax to the signal collector. If the index value is within the normal range, repeat step 2.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) The present invention provides a biofeedback system integrating EEG, EMG, and pulse wave analysis, and its analysis method, which is particularly useful for improving anxiety symptoms. Specifically, different state indices can be obtained by analyzing the three biosignals of EEG, EMG, and pulse wave: brain relaxation index, body relaxation index, and psychological relaxation index. Through continuous positive guidance from the feedback system, patients can gradually improve their anxiety.

[0046] (2) This method converts complex EEG signals into a percentage index that is easy for clinicians to identify. It is simple and easy to understand and displays the patient's condition in real time, thereby greatly improving the effectiveness of biofeedback therapy and helping patients recover.

[0047] (3) This method decomposes EEG signals into frequency domain amplitudes through real-time filtering, which has strong anti-interference ability and more accurate and reliable evaluation results. The key parameters in this method are obtained by combining labeled large samples with machine learning, which has higher evaluation accuracy and wider applicability.

[0048] (4) The three types of biological signal responses of patients are characterized by different items, so the perspective is more three-dimensional. When evaluating the patient's condition, a comprehensive judgment can be made through multiple dimensions and multiple indicators, thereby improving the accuracy of the evaluation. Accurate evaluation means that it can provide better basic support for the detailed training of patients. For example, in the symptoms of anxiety, rapid breathing and increased heart rate can be analyzed through pulse wave analysis to obtain the patient's condition. When people are nervous or restless, they will unconsciously tense their muscles. At this time, the patient's condition can be obtained through electromyography analysis. When the patient's concentration is reduced, weak or tired, the patient's condition can be obtained through electroencephalography analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The disclosure of the present invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. In the accompanying drawings, the same reference numerals are used to refer to the same components. Among them:

[0050] Figure 1 This is a hardware block diagram of the signal collector in the present invention;

[0051] Figure 2 It is a software block diagram of the signal collector in the present invention;

[0052] Figure 3 This is a hardware block diagram of the signal receiver in the present invention;

[0053] Figure 4 is a software block diagram of the biofeedback instrument software in the present invention;

[0054] Figure 5 is a software flow chart of the signal collector in the present invention;

[0055] Figure 6 is a software flow chart of the biofeedback instrument software in the present invention;

[0056] Figure 7 Schematic diagram of the comparison between the original EEG and the EEG frequency domain graph in the present invention;

[0057] Figure 8 The waveform diagram of each EEG band in the present invention;

[0058] Figure 9 This is a graph of the brain relaxation index according to the present invention. DETAILED DESCRIPTION

[0059] In order to make the technical means, creative features, objectives and effects of the present invention easier to understand, the present invention will now be further described in detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the relevant components of the present invention.

[0060] According to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art may propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific embodiments and drawings are merely illustrative of the technical solution of the present invention and should not be regarded as the entire invention or as a limitation or restriction of the technical solution of the present invention.

[0061] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0062] like Figures 1 to 4 As shown, a biofeedback system for EEG, EMG and pulse wave and its analysis method include a signal collector, a signal receiver and biofeedback instrument software, which are responsible for collecting biological signals, receiving signals, and analyzing and processing signals respectively.

[0063] The signal collector is used to collect three signals: EEG, EMG and pulse wave. Unlike the signal collectors on the market that only collect signals of a single indicator, the signal collector of the present invention can collect three signals including EEG, EMG and pulse wave.

[0064] The signal receiver is used to receive the biological signal data from the signal collector and forward the received data to the biofeedback instrument software on the PC, which processes and analyzes the data. Finally, different state indices are obtained by analyzing the three biological signals of electroencephalogram, electromyography and pulse wave: brain relaxation index, body relaxation index and psychological relaxation index.

[0065] Among them, the signal collector has a common mode rejection ratio greater than 100dB, system noise less than 2uVpp, impedance greater than 20MΩ, and a measurement range of 2μV to 5000μV. The high-performance design can accurately collect EEG and EMG signals.

[0066] See also Figure 1 The signal collector includes a main controller, a Bluetooth cluster machine module, a voice circuit and a biological signal acquisition circuit. The Bluetooth cluster machine module and the biological signal acquisition circuit are respectively connected to the main controller. The biological signal acquisition circuit sends the collected EEG, EMG and pulse wave data to the main controller. The main controller sends the data to the signal receiver through the Bluetooth cluster machine module after pre-processing the data; the voice circuit is connected to the main controller, and the voice circuit plays voice according to the command of the biofeedback instrument software to guide the patient to adjust his state.

[0067] Furthermore, as a preferred solution, the biological signal acquisition circuit includes an EEG and EMG acquisition circuit and a pulse acquisition circuit composed of an analog front-end chip. The EEG and EMG acquisition circuit is used to collect the patient's EEG signals and EMG signals, and the pulse acquisition circuit is used to collect the patient's pulse wave signals. The EEG and EMG acquisition circuit is provided with an electrode detachment detection device.

[0068] The EEG and EMG acquisition circuit features scalp electrodes, frontal muscle electrodes, and a reference electrode. The scalp electrodes are connected to an analog front-end chip via low-pass filtering, while the frontal muscle electrodes are also connected to the chip via low-pass filtering. The reference electrode serves only as a reference electrode and can be installed in other locations on the body to detect signals, such as the left and right legs or the base of the left and right ears. Finally, it is connected to the analog front-end chip for real-time monitoring. The pulse acquisition circuit primarily uses a pulse ear clip placed at the pulse location of the artery to collect the pulse signal. After amplification and filtering, the signal is amplified and connected to the main controller via an ADC.

[0069] In the above solution, the main controller can use a circuit board model STM32F103CBT6. The voice circuit mainly connects the voice chip to the main controller. The voice chip has voice recognition and Flash storage functions. The voice chip is connected to the speaker through the power amplifier circuit to provide voice broadcast function for patients.

[0070] See also Figure 2 The software of the signal collector includes a collector control module, a command parsing module, a Bluetooth transceiver module, a sound module, an EEG reading module, an EMG reading module, a pulse reading module and a data processing module; the collector control module is connected to the data processing module, the collector control module is respectively connected to the command parsing module, the Bluetooth transceiver module and the sound module, and the data processing module is respectively connected to the EEG reading module, the EMG reading module and the pulse reading module; the Bluetooth transceiver module is responsible for sending and receiving Bluetooth data, the sound module is responsible for playing and stopping the sound, the command parsing module is responsible for parsing the Bluetooth data, the EEG, EMG and pulse reading modules are respectively responsible for reading the corresponding signals, and the data processing module is responsible for parsing and processing the EEG, EMG and pulse.

[0071] The EEG reading module is used to read signals from scalp brain electrodes, the EMG reading module is used to read signals from frontal muscle electrodes, the pulse reading module is used to read pulse signals, the Bluetooth transceiver module is used to send and receive data from the Bluetooth module, and the sound module is used to manage voice information from the voice chip.

[0072] See also Figure 3The signal receiver consists of four Bluetooth host modules and a USB-to-serial circuit. The Bluetooth host module receives biosignal data from the signal collector. In this solution, one signal receiver can connect to up to 20 signal collectors for simultaneous voice guidance. The USB-to-multi-serial circuit forwards the data received by the four Bluetooth host modules to the biofeedback instrument software on the PC, which processes and analyzes the data.

[0073] See also Figure 4 The biofeedback instrument software includes a control module and an interactive module. The control module is connected to the interactive module. The control module is connected to the log module, display module, data receiving module, analysis and processing module, storage and playback module. The interactive module is connected to the patient management module, video training module, and game training module.

[0074] The log module is used to display the number of operations and the content of the operations; the display module is used to display the data collected by the signal; the data receiving module is used to receive the biological signal data; the analysis and processing module is used to process and analyze the EEG, EMG and pulse wave data to generate the brain relaxation index L H , body relaxation index L B and psychological relaxation index L M The storage and playback module is used to store system data and replay biosignal feedback during operation. The patient management module is used to authenticate and identify patient information. The video training module includes multiple video training tutorials and uses a voice module to guide patients through training to facilitate signal collection. The game training module includes game programs to help patients stabilize their emotions and facilitate smooth biosignal collection.

[0075] See also Figure 5 and Figure 6 , an analysis method for an electroencephalogram, electromyography and pulse wave biofeedback system, which collects the patient's electroencephalogram, electromyography and pulse wave signals through a signal collector; a signal receiver is used to receive the biological signal data from the signal collector, and forward the received data to the biofeedback instrument software on a PC, which processes and analyzes the data; finally, different state indices are obtained through analysis of the three biological signals of electroencephalogram, electromyography and pulse wave: brain relaxation index, body relaxation index and psychological relaxation index.

[0076] See also Figure 5 , the acquisition process of the signal collector is:

[0077] Step 1: The patient wears the terminal acquisition device, waits for the voice broadcast instructions, and starts playing the voice; it is mainly to guide the patient to perform corresponding operations, and can conduct video training and game training, as well as corresponding action guidance.

[0078] Step 2: The hardware of the signal collector is initialized and runs, waiting for the acquisition start command. If the acquisition command starts, all data of the analog front end are read. If the acquisition command does not start, it continues to wait for the acquisition start command;

[0079] Step 3: Analyze the EEG data and EMG data; in addition, read the pulse wave value through the ADC, combine the EEG data, EMG data, and pulse wave value data, and combine all the data into one frame of data;

[0080] Step 4: Send the combined data to the signal receiver via Bluetooth, and then wait for the end command issued by the receiving system. If the end command is not received, continue to wait for the acquisition start command.

[0081] See also Figure 6 , the acquisition process of the biofeedback instrument software is:

[0082] Step 1: The system matches the patient's identity, sends a connection instruction, connects to the signal collector, and waits for a successful connection. If the connection is successful, the system sends a collection command. If not, the system continues to wait.

[0083] Step 2: Wait for data to be received, split the EEG data, EMG data, and pulse wave data, calculate the brain relaxation index, body relaxation index, and psychological relaxation index through algorithms, and display each index on the display;

[0084] ① When processing EEG data, first filter the EEG data to remove irrelevant signals outside the effective frequency band, and then perform fast Fourier transform (FFT) to obtain the amplitude P at n Hz. n ,See Figure 7 :

[0085]

[0086] Among them, T e is the FFT processing period, EEG(t) is the amplitude at time t,

[0087] Define the frequency coefficient sequences Cn and Dn, and get the frequency energy ratio R as

[0088]

[0089] Among them, the maximum value of n is N, the value range of N is [1, Fs / 2], Fs is the signal sampling rate,

[0090] The normalized brain-related index L H for,

[0091]

[0092] Among them, Th is the threshold, Cn, Dn and Th are adjustable coefficients based on the labeled EEG data and the corresponding EEG index data, and are obtained through deep learning. The brain-related index can be manually set and selected; this application uses the brain relaxation index, which is used to treat anxiety.

[0093] In this embodiment, EEG data of a large number of anxiety disorder subjects and non-anxiety disorder subjects were first collected. After filtering and Fourier transformation, the frequency parts of theta wave (4-8 Hz), alpha wave (8-12 Hz), and beta wave (13-30 Hz) were integrated and inverse Fourier transformed to obtain the energy and time domain waveforms of each band. Figure 8 Then, based on the patient's anxiety state, the energy of each band is clustered into data of different anxiety levels. The clustering method of machine learning is applied to obtain the optimal Cn, Dn sequence and Th, which are used to calculate different percentage brain relaxation indexes L. H To distinguish different levels of anxiety, Th is 0.3, and the sequences of Cn and Dn are:

[0094]

[0095]

[0096] Figure 9 When applying this method, L H Example waveform graph of an exponential.

[0097] It should be noted that when processing EEG data, another method can be used, that is, first perform a 0.5Hz-60Hz bandpass filter on the EEG data per second to filter out irrelevant signals outside the effective frequency band, and then perform a fast Fourier transform (FFT) to calculate the energy E of the α wave (8-12Hz) α ,

[0098] Then the real-time value of α wave energy E α for:

[0099] Among them, P8 refers to the amplitude of 8Hz frequency, P9 refers to the amplitude of 9Hz frequency, and the rest are the same until P 12 ;

[0100] When performing fast Fourier transform (FFT), the frequency parts of theta wave (4-8Hz), alpha wave (8-12Hz), and beta wave (13-30Hz) are integrated respectively to obtain the energy of each band.

[0101] Theta wave activity (4-8Hz) typically represents a more daydreamy, spaced-out mental state, associated with low mental productivity. At very slow levels, theta brainwave activity is a very relaxed state, representing the twilight zone between wakefulness and sleep. Alpha waves (8-12Hz) are generally associated with states of relaxation. Activity in the lower half of this range largely represents the brain entering an idle state, relaxed and somewhat detached. Beta waves (13-30Hz) are associated with states of mental, intellectual activity, and outward focus. Low-frequency activity in this band (such as sensorimotor rhythms, or SMRs) is associated with attention during a relaxed state.

[0102] Before formal treatment, there is a baseline assessment process, during which the patient is in an idle state. The present invention collects the patient's alpha wave energy mean in the spatial state as the threshold E T , set using the planned threshold ratio R.

[0103] Brain relaxation index L H for: Among them, R is 50-80, E T is the mean α wave energy;

[0104] The planned threshold ratio (R) varies depending on the patient. The R value for the initial treatment is determined by the physician based on the patient's condition. If the patient's anxiety level is low, the difficulty level can be set higher, with R set to 70-80. Otherwise, the difficulty level can be lowered, with R set to 50-70. With subsequent treatments, the R value is gradually increased, and repeated treatments can continuously improve the patient's state of relaxation and alleviate anxiety symptoms.

[0105] ② When processing EMG data, this method collects EMG data from a large number of subjects, divides the subjects into 6 categories according to the diagnosed anxiety scores, and uses the average EMG root mean square value of different categories as the classification EMG value corresponding to the score, as shown in the following table.

[0106] Surface electromyography root mean square value Anxiety score Body relaxation index 1.5 0 100 5 28 72 10 45 55 20 61 39 50 83 17 100 100 0

[0107] Then, curve fitting is performed on the corresponding data in the table to obtain the body relaxation index L B Calculation formula:

[0108]

[0109] Among them, EMG is the root mean square value of electromyography;

[0110] When L B The higher the value, the higher the myoelectric level, and the more the body is in a state of tension. Conversely, the lower the myoelectric level, the more relaxed the body is.

[0111] ③ When processing the pulse wave, first perform a 0.5Hz-4.5Hz bandpass filter on the pulse wave data, that is, filter out invalid signals outside the 30bpm-245bpm range, and then perform frequency domain analysis and time domain analysis on the filtered waveform. When performing frequency domain analysis, first perform a fast Fourier transform (FFT) and take the frequency point with the highest amplitude F H (Hz), heart rate HR=60*F H .

[0112] Then the LF value expressing the overall activity of the sympathetic nerves and the HF value expressing the activity of the parasympathetic nerves were calculated.

[0113]

[0114]

[0115] Among them, P n Refers to the amplitude of the frequency in n Hz;

[0116] The LF / HF ratio can be used as an indicator of stress (sympathetic nervous system activity). During relaxation, when the parasympathetic nervous system is activated, both the HF component reflecting respiratory fluctuations and the LF component reflecting blood pressure fluctuations appear. Occasionally, the HF component decreases and the LF component increases. Therefore, during relaxation, the HF component becomes larger, resulting in a smaller LF / HF ratio. Therefore, the closer the LF / HF ratio is to 1, the more relaxed the person is; a larger LF / HF ratio indicates greater stress and less relaxation.

[0117] It is stipulated that LF / HF equals 1 for the best state, that is, 100 points, and 3 for the worst state, that is, 0 points. The psychological relaxation index L M for:

[0118]

[0119] Here, x is the value of LF / HF.

[0120] Step 3: By comparing with the standard index range, determine whether the displayed index value is too high. If it is too high, send a voice reminder to relax to the signal collector. If the index value is within the normal range, repeat step 2.

[0121] In summary, a biofeedback system integrating EEG, EMG, and pulse wave analysis, and its analysis method, is particularly suitable for improving anxiety symptoms. Specifically, different state indices can be derived from these three biosignals: brain relaxation index, physical relaxation index, and psychological relaxation index. Through continuous positive guidance from the feedback system, patients can gradually improve their anxiety.

[0122] The three types of biosignal responses in patients are characterized by different items, so the perspective is more three-dimensional. When evaluating the patient's condition, a comprehensive judgment can be made through multiple dimensions and multiple indicators, thereby improving the accuracy of the evaluation. Accurate evaluation means that it can provide better basic support for the refinement of patient training. For example, in the symptoms of anxiety, rapid breathing and increased heart rate can be analyzed through pulse wave analysis to obtain the patient's condition. When people are nervous or restless, they will unconsciously tense their muscles. At this time, the patient's condition can be obtained through electromyography analysis. When concentration is reduced, weak or tired, the patient's condition can be obtained through electroencephalogram analysis.

[0123] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A biofeedback system for electroencephalogram, electromyography and pulse wave, characterized by: The system includes a signal collector, a signal receiver and biofeedback instrument software. The signal collector is used to collect three types of signals: EEG, EMG and pulse wave. The signal receiver is used to receive the biological signal data from the signal collector and forward the received data to the biofeedback instrument software on the PC. The biofeedback instrument software processes and analyzes the data. Finally, different state indices are obtained through the analysis of the three biological signals: brain relaxation index, body relaxation index and psychological relaxation index. The acquisition process of the biofeedback instrument software is as follows: Step 1: The system matches the patient, sends a connection instruction, connects to the signal collector, and waits for a successful connection. If the connection is successful, the acquisition command is sent. If not, the system continues to wait. Step 2: Wait for data to be received, split the EEG data, EMG data, and pulse wave data, calculate the brain relaxation index, body relaxation index, and psychological relaxation index through algorithms, and display each index on the display screen; Step 3: Based on the comparison with the standard index range, determine whether the displayed index value is too high. If it is too high, send a voice reminder to relax to the signal collector. If the index value is within the normal range, repeat step 2. In step 2, when processing EEG data, the EEG data is first filtered to remove irrelevant signals outside the effective frequency band, and then a fast Fourier transform is performed to obtain the amplitude at n Hz. : ； in, is the FFT processing period, EEG(t) is the amplitude at time t, Define the frequency coefficient sequences Cn and Dn, and get the frequency energy ratio R as ； Among them, the maximum value of n is N, the value range of N is [1, Fs / 2], Fs is the signal sampling rate, Normalized brain relaxation index for, ； Among them, Th is the threshold, and Cn, Dn, and Th are all obtained through deep learning based on the labeled EEG data and the corresponding EEG index data; When processing electromyographic data, the real-time root mean square value of electromyography is calculated according to the following formula to obtain the body relaxation index L B： ； Among them, EMG is the root mean square value of electromyography; When processing the pulse wave, first perform a 0.5Hz-4.5Hz bandpass filter on the pulse wave data, that is, filter out invalid signals outside the 30bpm-245bpm range, and then perform frequency domain analysis and time domain analysis on the filtered waveform. When performing frequency domain analysis, first perform a fast Fourier transform (FFT) and take the frequency point with the highest amplitude F H (Hz), heart rate HR=60*F H , Then the LF value expressing the overall activity of the sympathetic nerves and the HF value expressing the activity of the parasympathetic nerves were calculated. ； ； If LF / HF is equal to 1, which is the best state, i.e. 100 points, and 3 is the worst state, i.e. 0 points, then the psychological relaxation index L M for: ； Where x is the value of LF / HF, The Th is 0.3, and the Cn and Dn are: ； 。 2. The electroencephalogram, electromyography and pulse wave biofeedback system according to claim 1, characterized in that: The biological signal acquisition circuit has a common mode rejection ratio greater than 100dB, a system noise less than 2uVpp, an impedance greater than 20MΩ, and a measurement range of 2μV to 5000μV.

3. The electroencephalogram, electromyography and pulse wave biofeedback system according to claim 1, characterized in that: The signal collector includes a main controller, a Bluetooth cluster machine module, a voice circuit and a biological signal acquisition circuit. The Bluetooth cluster machine module and the biological signal acquisition circuit are respectively connected to the main controller. The biological signal acquisition circuit sends the collected EEG, EMG and pulse wave data to the main controller. After pre-processing the data, the main controller sends the data to the signal receiver through the Bluetooth cluster machine module; the voice circuit is connected to the main controller, and the voice circuit plays voice according to the commands of the biofeedback instrument software to guide the patient to adjust his state.

4. The electroencephalogram, electromyography and pulse wave biofeedback system according to claim 3, characterized in that: The biological signal acquisition circuit includes an electroencephalogram and myoelectric acquisition circuit and a pulse acquisition circuit composed of an analog front-end chip. The electroencephalogram and myoelectric acquisition circuit is used to collect the patient's electroencephalogram signals and myoelectric signals, and the pulse acquisition circuit is used to collect the patient's pulse wave signals. The electroencephalogram and myoelectric acquisition circuit is provided with an electrode detachment detection device.

5. The electroencephalogram, electromyography and pulse wave biofeedback system according to claim 1, characterized in that: The software of the signal collector includes a collector control module, a command parsing module, a Bluetooth transceiver module, a sound module, an EEG reading module, an EMG reading module, a pulse reading module and a data processing module; the collector control module is connected to the data processing module, the collector control module is respectively connected to the command parsing module, the Bluetooth transceiver module and the sound module, and the data processing module is respectively connected to the EEG reading module, the EMG reading module and the pulse reading module; the Bluetooth transceiver module is responsible for sending and receiving Bluetooth data, the sound module is responsible for playing and stopping the sound, the command parsing module is responsible for parsing the Bluetooth data, the EEG, EMG and pulse reading modules are respectively responsible for reading the corresponding signals, and the data processing module is responsible for parsing and processing the EEG, EMG and pulse; The biofeedback instrument software includes a control module and an interaction module. The control module is connected to the interaction module. The control module is connected to the log module, display module, data receiving module, analysis and processing module, storage and playback module. The interaction module is connected to the patient management module, video training module, and game training module. The analysis and processing module is used to process and analyze EEG, EMG and pulse wave data to generate a brain relaxation index L. H , body relaxation index L B and psychological relaxation index L M .

6. The method for analyzing an electroencephalogram, electromyography, and pulse wave biofeedback system according to any one of claims 1 to 5, characterized in that: The signal collector collects three types of signals from the patient: electroencephalogram (EEG), electromyography (EMG), and pulse wave; the signal receiver is used to receive the biological signal data from the signal collector and forward the received data to the biofeedback instrument software on the PC, which processes and analyzes the data; finally, different state indices are obtained through analysis of the three biological signals: brain relaxation index, body relaxation index, and psychological relaxation index.

7. The method for analyzing an electroencephalogram, electromyography and pulse wave biofeedback system according to claim 6, characterized in that: The acquisition process of the signal collector is as follows: Step 1: The patient wears the terminal collection device, waits for the voice broadcast instruction, and starts playing the voice; Step 2: The hardware of the signal collector is initialized and runs, waiting for the acquisition start command. If the acquisition command starts, all data of the analog front end are read. If the acquisition command does not start, it continues to wait for the acquisition start command; Step 3: Analyze the EEG data and EMG data; in addition, the ADC reads the pulse wave value, combines the EEG data, EMG data, and pulse wave value data, and combines all the data into one frame of data; Step 4: Send the combined data to the signal receiver via Bluetooth, and then wait for the end command issued by the receiving system. If the end command is not received, continue to wait for the acquisition start command.

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