Wireless high-density electroencephalogram and myoelectricity hybrid synchronous acquisition and monitoring system

Abstract

The invention discloses a wireless high-density electroencephalogram and myoelectricity hybrid synchronous acquisition and monitoring system, and belongs to the field of human-computer interaction. The wireless high-density electroencephalogram and myoelectricity mixed synchronous collecting and monitoring system is composed of three parts, namely a high-density electroencephalogram and surface myoelectricity signal collecting system, a base station system and an upper computer system. The high-density electroencephalogram and surface electromyogram signal acquisition system is responsible for acquisition and primary processing of signals, and the base station system performs data interaction with the high-density electroencephalogram and surface electromyogram signal acquisition system in a wireless transmission mode and transmits the processed signals to the upper computer system for further analysis and display. According to the invention, a wireless and distributed architecture and a high-channel-number data acquisition scheme are integrated, the problems of efficient acquisition and synchronous processing of electroencephalogram and myoelectricity mixed signals are emphatically solved, and the application range in the fields of brain-computer interfaces, virtual reality, neural rehabilitation, cognitive training and the like is widened.

Description

Technical Field

[0001] The invention relates to the field of human-computer interaction, and specifically to a synchronous collection, transmission and analysis system for human brain electrical signals and surface physiological myoelectric signals, in particular to a wireless synchronous collection and analysis system for mixed brain electrical and myoelectric data. Background Art

[0002] Electroencephalogram (EEG) is a bioelectric signal generated by the activity of brain neurons, which can reflect the important physiological state and neural activity information of the brain. EEG signals have the advantages of non-invasiveness, non-traumaticity, and high temporal resolution. Usually, EEG signals are recorded by placing monopolar or bipolar electrodes on the scalp. However, since the activation state of neurons in different areas of the brain is not uniform, and the position of the electrode has a great influence on signal acquisition, the use of electrodes with a small number of channels will produce different EEG activity results due to differences in the placement of electrodes when extracting EEG signals. This makes it difficult to obtain EEG signals of sufficient accuracy in certain tasks such as precise brain area function analysis and cognitive status assessment, which in turn limits the accurate decoding and analysis of complex brain activities.

[0003] Surface electromyogram (sEMG) is a bioelectric signal generated by human neuromuscular activity. It can reflect important physiological status and health information of the human body and has the advantages of being non-invasive, non-traumatic, and easy to operate. Surface electromyography is usually performed by placing a single electrode or two electrodes on the muscle belly to extract electromyographic signals. However, the activation state of skeletal muscles is uneven during exercise. When extracting signals with fewer electrode channels, the signals will differ due to different electrode placement positions, making it difficult to complete tasks such as accurate muscle activation analysis and muscle force estimation.

[0004] High-density electroencephalography (HD-EEG) acquisition uses a three-dimensional matrix electrode array to cover the entire scalp area. Through the high-density electrode layout, HD-EEG can obtain richer time-frequency and spatial domain information in different brain regions, generate spatial distribution images of brain activity, and make up for the low spatial resolution of traditional few-channel EEG. As a means of objectively quantifying brain electrical activity, high-density EEG acquisition not only has important applications in neuroscience fields such as epilepsy monitoring and cognitive function research, but also shows broad application prospects in fields such as brain-computer interface, virtual reality, neurorehabilitation, and cognitive training. High-density surface electromyography (HD-sEMG) uses a two-dimensional matrix surface electrode to cover the entire muscle skin area. High-density surface electromyography can obtain the time-frequency and spatial domain information of the target muscle, and obtain a visualization of the corresponding activation area of ​​skeletal muscle, overcoming the disadvantage of low spatial resolution of ordinary electrodes. The synchronous acquisition of high-density EEG and high-density surface electromyography is an important technical means to carry out mechanistic refinement analysis of the human neuromusculoskeletal system and human-computer interactive control. In view of the demand for synchronous acquisition of human EEG and EMG, the shortcomings of existing EEG and EMG hybrid acquisition technology are mainly reflected in the following four aspects:

[0005] (1) In response to the wireless demand for EEG and surface electromyography data collection, existing wireless acquisition devices have problems such as insufficient number of channels (usually less than 128 channels), low sampling frequency (less than 1000Hz), and insufficient synchronization accuracy (delay exceeding 1 millisecond). It is difficult to achieve real-time synchronous acquisition of EEG and surface electromyography signals in multiple scenarios. These limitations seriously affect the analysis effect and practical application of EEG and electromyography mixed data. In addition, wired acquisition of electromyography and EEG signals is more likely to cause mutual interference.

[0006] (2) At present, the acquisition of electromyography and electroencephalography is usually carried out through separate channels for analog amplification, filtering and analog-to-digital conversion. This leads to problems such as large equipment size, high power consumption and restricted movement of subjects during the test.

[0007] (3) For applications such as brain-computer interfaces, motion control, and rehabilitation training, relying solely on a single EEG or surface electromyography signal cannot fully reflect the user's neuromuscular activity status.

[0008] (4) Since EEG signals are low-frequency, low-amplitude electrical signals, they will be coupled to higher-amplitude industrial frequency interference from the surrounding environment during EEG signal acquisition. The existing front-end analog circuits lack the characteristics of high gain, high common-mode rejection ratio and low noise.

[0009] In response to the above problems, the present invention is committed to developing a new hybrid synchronous acquisition system of EEG and EMG, which realizes the synchronous acquisition of high-density EEG and high-density surface EMG through a wireless body area network, has a low-power, wearable design, supports data acquisition with high channel count and high sampling rate, and ensures high-precision and low-latency transmission of signals. In addition, the system effectively reduces the interference between EEG and EMG signals through a time synchronization mechanism, and can perform real-time monitoring and data analysis in multiple scenarios, which is suitable for fields such as neuroscience, motion control and rehabilitation training. Summary of the invention

[0010] The present invention aims to solve the problems of limited channel number, time deviation of EEG and EMG data, poor signal quality and other problems in signal acquisition and usage experience in EEG and EMG hybrid acquisition technology. It provides a data acquisition solution that integrates wireless, distributed architecture and high channel number, focusing on solving the problems of efficient acquisition and synchronous processing of EEG and EMG hybrid signals, and broadening its application in brain-computer interface, virtual reality, neurorehabilitation and cognitive training.

[0011] The technical solution of the present invention:

[0012] A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system, the wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system consists of three parts: a high-density EEG and surface EMG signal acquisition system, a base station system and a host computer system; the high-density EEG and surface EMG signal acquisition system is responsible for signal acquisition and preliminary processing, the base station system exchanges data with the high-density EEG and surface EMG signal acquisition system through wireless transmission, and transmits the processed signal to the host computer system for further analysis and display;

[0013] 1) The high-density EEG and surface EMG signal acquisition system includes: a high-density EEG electrode array, a high-density EMG electrode array, a reference electrode, and a signal acquisition and transmission unit;

[0014] 1.1) High-density EEG electrode arrays are placed at specific locations on the scalp to collect EEG signals and record the electrical activity of the brain;

[0015] 1.2) The high-density EMG electrode array is arranged on the surface of skeletal muscle, and the number of contacts of the high-density EMG electrodes in the high-density EMG electrode array is customized according to needs;

[0016] 1.3) The reference electrode adopts a wristband design and is fixed to the tendons of various skeletal muscles of the human body; the reference electrode is connected to the signal acquisition and transmission unit through a lead wire;

[0017] 1.4) The signal acquisition and transmission unit includes an EEG signal acquisition and transmission unit and a surface electromyography signal acquisition and transmission unit. Each signal acquisition and transmission unit includes a signal acquisition chip, a nine-axis acceleration sensor MPU-9250, a microcontroller STM32, a wireless transmission module, a power control and battery module; the wireless transmission module includes a wireless microcontroller CC3235 and a radio frequency transceiver CC2500;

[0018] Signal acquisition chip: used to amplify and filter the collected EEG signals and surface EMG signals, and convert the analog signals into high-resolution digital signals through a 24-bit analog-to-digital converter, completing the amplification, filtering and digital processing of 64-channel EEG signals or surface EMG signals;

[0019] Nine-axis acceleration sensor MPU-9250: used to collect acceleration data of the human body, provide motion-related data support for the synchronization of EEG signals and surface electromyography signals, and provide a data basis for monitoring posture and motion trajectory;

[0020] Microcontroller STM32: used to integrate the EEG and surface electromyography data processed by the signal acquisition chip and the acceleration data collected by the nine-axis acceleration sensor MPU-9250, and transmit the integrated data to the wireless microcontroller CC3235;

[0021] Wireless microcontroller CC3235: communicates with the base station system in real time through the RF wireless circuit, and is used to synchronously package and send the integrated data from the microcontroller STM32 to the base station system. At the same time, it sends the synchronization control instructions sent by the base station system to the microcontroller STM32 to control the start and stop of signal acquisition and time synchronization of each signal acquisition and transmission unit;

[0022] RF transceiver CC2500: used to receive the synchronization control instructions of the base station system and send the synchronization control instructions of the base station system to the wireless microcontroller CC3235;

[0023] The power control and battery module includes a power control unit and a battery unit: The power control unit adopts a low-voltage difference linear voltage regulator circuit design, which ensures the stability of the power supply voltage by adjusting the linear output of the P-MOS tube and minimizes power frequency interference; the battery unit uses a lithium-ion battery to power the entire signal acquisition and transmission unit, which can support the signal acquisition and transmission unit to work continuously for more than four hours and the standby time exceeds ten hours, ensuring the smooth progress of long-term experiments. It can also be charged through the power management and charging module of the base station system;

[0024] 2) The base station system includes a synchronization control module, a wireless routing module, a power management and charging module, and a host computer communication network port;

[0025] 2.1) The synchronization control module includes a microcontroller MCU, a radio frequency transceiver CC2500 and an external synchronization trigger unit; the synchronization control module periodically sends synchronization control signals to all signal acquisition units to ensure that the sampling clocks of each acquisition unit are accurately synchronized; the synchronization control module can also perform synchronous acquisition with external devices to ensure the timing alignment of multi-modal data;

[0026] Microcontroller MCU: used for real-time control of synchronous control signal transmission;

[0027] RF transceiver CC2500: used to receive the synchronization control signal of the microcontroller MCU and transmit the synchronization control signal to the signal acquisition and transmission unit, and perform synchronization calibration of each signal acquisition and transmission unit;

[0028] External synchronous trigger unit: It has a synchronous input interface and a synchronous output interface. The synchronous input interface is connected to an external device through a connector, receives input signals from the external device and transmits them to the microcontroller MCU, thereby controlling the start of a high-density EEG and surface electromyography signal acquisition system, and performs synchronous acquisition in cooperation with external devices; the synchronous output interface is connected to an external device, receives synchronous control signals from the microcontroller MCU, and controls the start of an external device for synchronous acquisition; the external device includes a motion capture system or other biological signal equipment;

[0029] 2.2) The wireless routing module is used to receive data sent by the wireless microcontroller CC3235 of the signal acquisition and transmission unit, and transmit the data to the host computer system through a wired network, supporting the synchronous acquisition and transmission of multi-channel EEG signals and surface electromyography signals;

[0030] 2.3) The power management and charging module provides power for the base station system and provides remote charging function for the signal acquisition and transmission unit. It ensures long-term stable operation of the equipment by connecting to an AC power source or a lithium battery. The power management and charging module also ensures power stability to prevent data loss or equipment damage caused by overload and unstable power supply.

[0031] 3) The host computer system includes a data preprocessing and display module, a high-density EEG and EMG decoding algorithm module, and a computer program for integrating data storage, processing, analysis and display based on the EEG and surface EMG data transmitted by the received base station system, so as to monitor and analyze the collected EEG and surface EMG data in real time;

[0032] 3.1) The data preprocessing and display module is used to filter and perform noise reduction preprocessing on the collected EEG signals and surface electromyography signals and to display the data: the EEG signals are subjected to noise reduction processing to remove electrooculogram and electromyography artifacts; the surface electromyography signals are subjected to bandpass filtering to remove low-frequency artifacts and high-frequency noise, and obtain target surface electromyography signals within the range of 20-500Hz; the preprocessed signal data can be simultaneously displayed on the user interface and stored in a designated path at a specified time according to user needs;

[0033] 3.2) The high-density EEG and EMG decoding algorithm module is used to decode the preprocessed EEG signals and surface EMG signals; signal decoding includes signal time-frequency domain feature extraction, motor nerve signal decoding, EEG network, EMG network and EEG and EMG coupling network feature analysis; the data after signal decoding includes EEG and surface EMG time-frequency domain features, network attribute features and motor unit time series spatiotemporal features related to the action or task; using the data after signal decoding, and selecting or fusing multimodal features as data input, based on support vector machine, convolutional neural network or long short-term memory network machine learning or deep learning algorithm, movement intention recognition, neuromuscular function evaluation and mechanical control are realized.

[0034] Furthermore, the signal time-frequency domain feature extraction is to extract the time-frequency domain features of the preprocessed EEG signal and surface electromyography signal:

[0035] A. Signal time domain feature extraction;

[0036] The time domain feature parameters are directly calculated based on the preprocessed signal. The time domain features include the following:

[0037] Mean: the average level of the signal;

[0038] Standard deviation: the degree of dispersion of the signal;

[0039] Peak-to-peak value: the amplitude range of the signal;

[0040] RMS value: the time domain energy of the signal;

[0041] Integrated EMG value: the total electrical activity of muscle contraction;

[0042] B. Signal frequency domain feature extraction;

[0043] The frequency domain features of the signal are extracted based on Fourier transform or wavelet transform. The frequency domain features include the following:

[0044] Power spectral density: the energy distribution of a signal at different frequencies;

[0045] Median frequency: the middle value of the signal spectrum energy distribution;

[0046] Average frequency: the arithmetic mean of the instantaneous frequency of the signal;

[0047] Average power frequency: The power-weighted average of each frequency component of the signal power spectral density.

[0048] Furthermore, the motor nerve signal decoding, i.e. the preprocessed surface electromyographic signal, is further used to extract the spatiotemporal characteristics of the motor unit time series based on the blind source separation algorithm and cluster analysis algorithm:

[0049] A. Signal expansion;

[0050] First, the surface electromyography signals of each channel after preprocessing are centered, that is, the average value of each channel is subtracted, and then the covariance matrix of the surface electromyography channel data after centering is calculated, and the covariance matrix is ​​decomposed by eigenvalue. The eigenvectors obtained by decomposition constitute a new feature space, and the signals are projected into the new feature space to eliminate the correlation between the signals.

[0051] B. Independent source signal extraction;

[0052] Recover independent source signals from the surface electromyographic signals after centering and decorrelation processing; use the FastICA algorithm to iteratively solve the mixing matrix based on maximizing non-Gaussianity, and further restore the collected surface electromyographic signals to the action potential sequence of the motor unit through the solved mixing matrix as the source signal, filter and decorrelate the restored source signals, and further use the cluster analysis algorithm to combine and classify the extracted source signals;

[0053] C. Motion unit feature extraction;

[0054] Based on the A and B processes, the spatiotemporal characteristics of the motor unit time series are extracted to obtain the number of motor units and the spatiotemporal characteristics of the firing rate of the surface electromyographic signal, identify the activation time and activation intensity of different muscles during the movement, and provide detailed information on each muscle activity unit.

[0055] Furthermore, the characteristics of EEG network, EMG network and EEG-MY coupling network are analyzed, that is, the network attribute characteristics are obtained based on the coherence analysis or causal analysis of the preprocessed EEG signal and surface EMG signal:

[0056] A. Coherence or causality analysis;

[0057] Using coherence analysis or causal relationship analysis on the preprocessed EEG signals and surface electromyography signals, calculating the coherence values ​​or causal relationship measurement values ​​between EEG channels, between electromyography channels, and between EEG and electromyography channels;

[0058] B. Network construction;

[0059] Based on the calculated coherence value or causal relationship measurement value, an EEG network, an EMG network and an EEG-MY coupling network are constructed. The network consists of many nodes and edges connecting the nodes. Each signal channel represents a node in the network, and the connection between channels represents an edge in the network. The size of the coherence value or causal relationship measurement value represents the connection strength of the edge in the network. The larger the coherence value or causal relationship measurement value, the closer the connection between the nodes, that is, the stronger the coupling between the channel signals.

[0060] C. Network feature extraction;

[0061] According to the constructed EEG network, EMG network and EEG-MY coupling network, the attribute characteristics of the network are calculated. The attribute characteristics include the following:

[0062] Node degree: the number of edges connecting a node to other nodes;

[0063] Node clustering coefficient: the proportion of the triangular network around a single node;

[0064] Global clustering coefficient: the average value of clustering coefficients of all nodes in the network;

[0065] Characteristic path length: the average shortest path length between all pairs of nodes in the network;

[0066] Global efficiency: the inverse of the average shortest path length.

[0067] Furthermore, the high-density EMG electrode supports a maximum of 640 contacts, and the contact distribution is optimized according to the muscle morphology. The high-density EMG electrode array is made of a flexible material with good air permeability and strong water absorption, which fits well with the skin to ensure signal stability.

[0068] Furthermore, the interface of the reference electrode adopts a hot-swappable design; the reference electrode wristband conducts electricity by being soaked in conductive liquid, and uses soft fabric with good air permeability and strong water absorption to ensure comfort for long-term wearing. The multi-button design on the reference electrode wristband allows multiple leads to be connected to the same wristband.

[0069] Furthermore, the wireless routing module can simultaneously transmit data with 10 signal acquisition and transmission units, that is, simultaneously transmit 640-channel signals and 60-channel acceleration signals; the wireless routing module supports remote sending of control commands to the high-density EEG and surface electromyography signal acquisition system, adjusts the settings of the signal acquisition and transmission units and modifies the system parameters, and can meet the transmission bandwidth requirement of a peak value of 300KSps; it can also receive control instructions issued by the host computer system; it adopts the Modbus communication protocol in the form of RS485 and a full-duplex working mode to ensure the flexibility of data exchange.

[0070] The main innovative features of the present invention include:

[0071] (1) Adopting a distributed structure and a unified system acquisition framework, the acquisition of EEG / EMG data with different channel numbers can be realized through the arbitrary integration of signal acquisition and transmission units;

[0072] (2) Based on the establishment of a synchronous acquisition system, the synchronous data acquisition of EEG and EMG signals is realized to provide multimodal signals for human movement intention recognition;

[0073] (3) Establish an EEG / EMG decoding algorithm, integrate EMG decomposition algorithms such as blind source separation and cluster analysis, feature extraction methods such as wavelet transform, and EEG network, EMG network, and EEG / EMG coupled network analysis methods such as coherence or causality analysis, and fuse multi-sensor and multi-modal features based on machine learning or deep learning algorithms to achieve motion intention recognition.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] (1) Multi-channel high-precision acquisition: The system can better synchronously acquire HD-EEG and HD-sEMG signals, and through optimized hardware architecture design, ensure high resolution and high precision of signal acquisition.

[0076] (2) Wireless distributed design: Based on wireless transmission technology, the front-end unit of the system is designed as a miniaturized structure, which supports long-term, stable and comfortable wearing, and realizes wireless communication with the base station, greatly improving the convenience of the system.

[0077] (3) Real-time display and storage of data: The host computer system supports real-time display and storage of signals. The signal preprocessing algorithm can effectively eliminate interference and ensure data quality.

[0078] (4) Decoding and analysis tools: The system’s integrated decoding algorithm module can accurately extract movement- or task-related features of EEG and EMG signals, providing strong support for neuromuscular function assessment and motor control research. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a schematic diagram of the framework of a wireless high-density EEG and EMG hybrid synchronous acquisition and monitoring system;

[0080] Figure 2 It is the functional block diagram of wireless high-density EEG / surface EMG acquisition equipment;

[0081] Figure 3 This is the appearance diagram of the wireless high-density EEG / surface EMG signal acquisition system;

[0082] Figure 4 It is the principle diagram of wireless high-density EEG / EMG hybrid decoding algorithm. DETAILED DESCRIPTION

[0083] like Figures 1 to 4 As shown, a wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system is composed of three parts: a high-density EEG and surface EMG signal acquisition system, a base station system and a host computer system; the high-density EEG and surface EMG signal acquisition system is responsible for signal acquisition and preliminary processing, the base station system exchanges data with the high-density EEG and surface EMG signal acquisition system through wireless transmission, and transmits the processed signal to the host computer system for further analysis and display;

[0084] 1) The high-density EEG and surface electromyography signal acquisition system includes: a high-density EEG (HD-EEG) electrode array, a high-density electromyography (HD-sEMG) electrode array, a reference electrode, and a signal acquisition and transmission unit;

[0085] 1.1) A high-density EEG electrode array is arranged at a specific position on the scalp to collect EEG signals and record the electrical activity of the brain. In actual use, the high-density EEG electrode array is made into a high-density EEG collection cap and fixed on the surface of the scalp.

[0086] 1.2) The high-density EMG electrode array is arranged on the surface of skeletal muscle, and the number of contacts of the high-density EMG electrodes in the high-density EMG electrode array is customized according to needs;

[0087] 1.3) The reference electrode adopts a wristband design and is fixed to the tendons of various skeletal muscles of the human body; the reference electrode is connected to the signal acquisition and transmission unit through a lead wire;

[0088] 1.4) The signal acquisition and transmission unit includes an EEG signal acquisition and transmission unit and a surface electromyography signal acquisition and transmission unit. Each signal acquisition and transmission unit includes a signal acquisition chip, a nine-axis acceleration sensor MPU-9250, a microcontroller STM32, a wireless transmission module, a power control and battery module; the wireless transmission module includes a wireless microcontroller CC3235 and a radio frequency transceiver CC2500;

[0089] Signal acquisition chip: used to amplify and filter the collected EEG signals and surface EMG signals, and convert the analog signals into high-resolution digital signals through a 24-bit analog-to-digital converter, completing the amplification, filtering and digital processing of 64-channel EEG signals or surface EMG signals;

[0090] Nine-axis acceleration sensor MPU-9250: used to collect acceleration data of the human body, provide motion-related data support for the synchronization of EEG signals and surface electromyography signals, and provide a data basis for monitoring posture and motion trajectory;

[0091] Microcontroller STM32: used to integrate the EEG and surface electromyography data processed by the signal acquisition chip and the acceleration data collected by the nine-axis acceleration sensor MPU-9250, and transmit the integrated data to the wireless microcontroller CC3235;

[0092] Wireless microcontroller CC3235: communicates with the base station system in real time through the RF wireless circuit, and is used to synchronously package and send the integrated data from the microcontroller STM32 to the base station system. At the same time, it sends the synchronization control instructions sent by the base station system to the microcontroller STM32 to control the start and stop of signal acquisition and time synchronization of each signal acquisition and transmission unit;

[0093] RF transceiver CC2500: used to receive the synchronization control instructions of the base station system and send the synchronization control instructions of the base station system to the wireless microcontroller CC3235;

[0094] The power control and battery module includes a power control unit and a battery unit: The power control unit adopts a low-voltage difference linear voltage regulator circuit design, which ensures the stability of the power supply voltage by adjusting the linear output of the P-MOS tube and minimizes power frequency interference; the battery unit uses a lithium-ion battery to power the entire signal acquisition and transmission unit, which can support the signal acquisition and transmission unit to work continuously for more than four hours and the standby time exceeds ten hours, ensuring the smooth progress of long-term experiments. It can also be charged through the power management and charging module of the base station system;

[0095] The high-density EEG and surface EMG signal acquisition system has achieved a miniaturized and portable design, ensuring wearing comfort and stability for long-term use. The design integrates the acquisition, processing and transmission functions of EEG and surface EMG signals, supports wireless communication, and is suitable for synchronous acquisition in multiple scenarios. The high-density EEG and surface EMG signal acquisition system can simultaneously acquire EEG and surface EMG signals, and perform high-precision data sampling from 16 to 24 bits. The signal sampling rate can reach 30KSps (30,000 samples per second), and supports up to 64 channels of data acquisition. The signal acquisition and transmission units can be freely combined according to the number of electrode channels in each high-density EEG electrode array and high-density EMG electrode array, and the number of electrodes is an integer multiple of 64. The data is sent to the base station system through the wireless transmission module, and Wi-Fi is used for low-latency data transmission. At the same time, the human body acceleration signal is obtained through the nine-axis acceleration sensor MPU-9250.

[0096] The high-density EEG (HD-EEG) and high-density surface EMG (HD-sEMG) signals collected by the high-density EEG and surface EMG signal acquisition system contain noise and artifacts. In order to prevent the high-frequency components in the signal from causing aliasing during the sampling process, the system first performs anti-aliasing filtering on the collected HD-EEG and HD-sEMG signals to remove useless frequency components above the Nyquist frequency to ensure the accuracy of the sampled signals. HD-EEG and HD-sEMG signals contain a large amount of low-frequency noise (such as electrode drift, motion artifacts) and high-frequency noise (such as power supply noise). These interferences are removed by high-pass and low-pass filtering. For HD-EEG signals, a 0.5Hz high-pass filter is often used to eliminate low-frequency drift, and a 50Hz or 60Hz low-pass filter is used to suppress power supply interference and high-frequency noise. For HD-sEMG signals, a 20Hz high-pass filter is used to remove low-frequency motion artifacts, and a 500Hz low-pass filter is used to remove high-frequency noise, retaining effective surface EMG signals in the range of 20-500Hz. HD-EEG signals are often interfered by biological artifacts such as electrooculogram (EOG) and electrocardiogram (ECG). Independent component analysis (ICA) or filter technology is used to remove these artifacts to retain pure brain electrical activity. For HD-sEMG signals, bandpass filtering can effectively reduce low-frequency and high-frequency noise in surface electromyography signals. Electrodes will cause baseline drift during long-term acquisition, especially in HD-EEG signals, where baseline drift may affect signal stability. These slow wave changes can be eliminated through baseline correction algorithms to ensure that the signal remains within a reasonable range.

[0097] The system automatically evaluates the signal quality of each channel and identifies abnormal channels due to poor electrode contact or other problems. If the signal quality of some channels is poor, the system will automatically mark and notify the user so that adjustments can be made or the data of these channels can be removed during subsequent processing.

[0098] 2) The base station system includes a synchronization control module, a wireless routing module, a power management and charging module, and a host computer communication network port;

[0099] 2.1) The synchronization control module includes a microcontroller MCU, a radio frequency transceiver CC2500 and an external synchronization trigger unit; the synchronization control module periodically sends synchronization control signals to all signal acquisition units to ensure that the sampling clocks of each acquisition unit are accurately synchronized; the synchronization control module can also perform synchronous acquisition with external devices to ensure the timing alignment of multi-modal data;

[0100] Microcontroller MCU: used for real-time control of synchronous control signal transmission;

[0101] RF transceiver CC2500: used to receive the synchronization control signal of the microcontroller MCU and transmit the synchronization control signal to the signal acquisition and transmission unit, and perform synchronization calibration of each signal acquisition and transmission unit;

[0102] External synchronous trigger unit: It has a synchronous input interface and a synchronous output interface. The synchronous input interface is connected to an external device through a connector, receives input signals from the external device and transmits them to the microcontroller MCU, thereby controlling the start of a high-density EEG and surface electromyography signal acquisition system, and performs synchronous acquisition in cooperation with external devices; the synchronous output interface is connected to an external device, receives synchronous control signals from the microcontroller MCU, and controls the start of an external device for synchronous acquisition; the external device includes a motion capture system or other biological signal equipment;

[0103] 2.2) The wireless routing module is used to receive data sent by the wireless microcontroller CC3235 of the signal acquisition and transmission unit, and transmit the data to the host computer system through a wired network, supporting the synchronous acquisition and transmission of multi-channel EEG signals and surface electromyography signals; the wireless routing module supports remote sending of control commands to the high-density EEG and surface electromyography signal acquisition system, adjusts the settings of the signal acquisition and transmission unit and modifies the system parameters, and can meet the transmission bandwidth requirement of a peak value of 300KSps; it can also receive control instructions issued by the host computer system; it adopts the Modbus communication protocol in the form of RS485 and a full-duplex working mode to ensure the flexibility of data exchange.

[0104] 2.3) The power management and charging module provides power for the base station system and provides remote charging function for the signal acquisition and transmission unit. It ensures long-term stable operation of the equipment by connecting to an AC power source or a lithium battery. The power management and charging module also ensures power stability to prevent data loss or equipment damage caused by overload and unstable power supply.

[0105] 3) The host computer system includes a data preprocessing and display module, a high-density EEG and EMG decoding algorithm module, and a computer program for integrating data storage, processing, analysis and display based on the EEG and surface EMG data transmitted by the received base station system, so as to monitor and analyze the collected EEG and surface EMG data in real time;

[0106] 3.1) The data preprocessing and display module is used to filter and perform noise reduction preprocessing on the collected EEG signals and surface electromyography signals and to display the data: the EEG signals are subjected to noise reduction processing to remove electrooculogram and electromyography artifacts; the surface electromyography signals are subjected to bandpass filtering to remove low-frequency artifacts and high-frequency noise, and obtain target surface electromyography signals within the range of 20-500Hz; the preprocessed signal data can be simultaneously displayed on the user interface and stored in a designated path at a specified time according to user needs;

[0107] 3.2) The high-density EEG and EMG decoding algorithm module is used to decode the preprocessed EEG signals and surface EMG signals; signal decoding includes signal time-frequency domain feature extraction, motor nerve signal decoding, EEG network, EMG network and EEG and EMG coupling network feature analysis; the data after signal decoding includes EEG and surface EMG time-frequency domain features, network attribute features and motor unit time series spatiotemporal features related to the action or task; using the decoded signal data and selecting or fusing multimodal features as data input, based on support vector machine (SVM), convolutional neural network (CNN) or long short-term memory network (LSTM) machine learning or deep learning algorithm, movement intention recognition, neuromuscular function evaluation and mechanical control are realized.

[0108] Furthermore, the signal time-frequency domain feature extraction is to extract the time-frequency domain features of the preprocessed EEG signal and surface electromyography signal:

[0109] A. Signal time domain feature extraction;

[0110] The time domain feature parameters are directly calculated based on the preprocessed signal. The time domain features include the following:

[0111] Mean: the average level of the signal;

[0112] Standard deviation: the degree of dispersion of the signal;

[0113] Peak-to-peak value: the amplitude range of the signal;

[0114] RMS value: the time domain energy of the signal;

[0115] Integrated EMG value: the total electrical activity of muscle contraction;

[0116] B. Signal frequency domain feature extraction;

[0117] The frequency domain features of the signal are extracted based on Fourier transform or wavelet transform. The frequency domain features include the following:

[0118] Power spectral density: the energy distribution of a signal at different frequencies;

[0119] Median frequency: the middle value of the signal spectrum energy distribution;

[0120] Average frequency: the arithmetic mean of the instantaneous frequency of the signal;

[0121] Average power frequency: The power-weighted average of each frequency component of the signal power spectral density.

[0122] The algorithm outputs the time-frequency domain features of EEG signals and surface EMG signals, which can be saved and exported in the form of files. Appropriate features can be selected or the time-domain and frequency-domain features can be fused according to actual application needs to improve the dimensionality and feature richness of HD-EEG and HD-sEMG signals.

[0123] Furthermore, the motor nerve signal decoding, i.e. the preprocessed surface electromyographic signal, is further used to extract the spatiotemporal characteristics of the motor unit time series based on the blind source separation algorithm and cluster analysis algorithm:

[0124] A. Signal expansion;

[0125] First, the surface electromyography signals of each channel after preprocessing are centered, that is, the average value of each channel is subtracted, and then the covariance matrix of the surface electromyography channel data after centering is calculated, and the covariance matrix is ​​decomposed by eigenvalue. The eigenvectors obtained by decomposition constitute a new feature space, and the signals are projected into the new feature space to eliminate the correlation between the signals.

[0126] B. Independent source signal extraction;

[0127] Recover independent source signals from the surface electromyographic signals after centering and decorrelation processing; use the FastICA algorithm to iteratively solve the mixing matrix based on maximizing non-Gaussianity, and further restore the collected surface electromyographic signals to the action potential sequence of the motor unit through the solved mixing matrix as the source signal, filter and decorrelate the restored source signals, and further use the cluster analysis algorithm to combine and classify the extracted source signals;

[0128] C. Motion unit feature extraction;

[0129] Based on the A and B processes, the spatiotemporal characteristics of the motor unit time series are extracted to obtain the number of motor units and the spatiotemporal characteristics of the firing rate of the surface electromyographic signal, identify the activation time and activation intensity of different muscles during the movement, and provide detailed information on each muscle activity unit.

[0130] The algorithm outputs information such as motor unit time series, motor unit number and firing rate of high-density surface electromyographic signals, which can be saved and exported in the form of files. According to actual application needs, appropriate motor unit features can be selected or feature fusion can be performed to provide an interpretable tool for neuromuscular analysis.

[0131] Furthermore, the characteristics of EEG network, EMG network and EEG-MY coupling network are analyzed, that is, the network attribute characteristics are obtained based on the coherence analysis or causal analysis of the preprocessed EEG signal and surface EMG signal:

[0132] A. Coherence or causality analysis;

[0133] Using coherence analysis or causal relationship analysis on the preprocessed EEG signals and surface electromyography signals, calculating the coherence values ​​or causal relationship measurement values ​​between EEG channels, between electromyography channels, and between EEG and electromyography channels;

[0134] B. Network construction;

[0135] Based on the calculated coherence value or causal relationship measurement value, an EEG network, an EMG network and an EEG-MY coupling network are constructed. The network consists of many nodes and edges connecting the nodes. Each signal channel represents a node in the network, and the connection between channels represents an edge in the network. The size of the coherence value or causal relationship measurement value represents the connection strength of the edge in the network. The larger the coherence value or causal relationship measurement value, the closer the connection between the nodes, that is, the stronger the coupling between the channel signals.

[0136] C. Network feature extraction;

[0137] According to the constructed EEG network, EMG network and EEG-MY coupling network, the attribute characteristics of the network are calculated. The attribute characteristics include the following:

[0138] Node degree: the number of edges connecting a node to other nodes;

[0139] Node clustering coefficient: the proportion of the triangular network around a single node;

[0140] Global clustering coefficient: the average value of clustering coefficients of all nodes in the network;

[0141] Characteristic path length: the average shortest path length between all pairs of nodes in the network;

[0142] Global efficiency: the inverse of the average shortest path length.

[0143] The algorithm outputs the network attribute characteristics of the EEG network, EMG network and EEG-MY coupling network, which can be saved and exported in the form of files. According to the actual application needs, appropriate features can be selected or feature fusion can be performed to quantify the information coupling between the central nervous system and peripheral muscles, reflecting the central-peripheral synergistic relationship during the execution of the action.

[0144] Furthermore, the high-density EMG electrode supports a maximum of 640 contacts, and the contact distribution is optimized according to the muscle morphology. The high-density EMG electrode array is made of a flexible material with good air permeability and strong water absorption, which fits well with the skin to ensure signal stability.

[0145] Furthermore, the interface of the reference electrode adopts a hot-swappable design; the reference electrode wristband conducts electricity by being soaked in conductive liquid, and uses soft fabric with good air permeability and strong water absorption to ensure comfort for long-term wearing. The multi-button design on the reference electrode wristband enables multiple leads to be connected to the same wristband, further improving the compatibility and flexibility of the system.

[0146] Furthermore, the wireless routing module can simultaneously transmit data with 10 signal acquisition and transmission units, that is, simultaneously transmit 640-channel signals and 60-channel acceleration signals; the wireless routing module supports remote sending of control commands to the high-density EEG and surface electromyography signal acquisition system, adjusts the settings of the signal acquisition and transmission units and modifies the system parameters, and can meet the transmission bandwidth requirement of a peak value of 300KSps; it can also receive control instructions issued by the host computer system; it adopts the Modbus communication protocol in the form of RS485 and a full-duplex working mode to ensure the flexibility of data exchange.

[0147] The use process of the wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system:

[0148] 1. Preparation stage;

[0149] (1) System inspection: Ensure that all parts of the system (including high-density EEG and surface EMG signal acquisition system, base station system, and host computer system) are in normal working condition and that the connection information between the parts is accurate.

[0150] (2) Wearable equipment: Fix the high-density EEG electrode array in the form of a collection cap at a specific position on the scalp to ensure that the electrodes are in close contact with the scalp to effectively collect the electrical activity of the brain; arrange the high-density EMG electrode array on the surface of skeletal muscle, optimize the arrangement of electrode contacts according to muscle morphology, and use flexible materials to ensure good fit with the skin to collect muscle electrical activity; the reference electrode adopts a wristband design and is fixed to the tendons of various skeletal muscles of the human body, and ensure that the lead is connected to the EMG wristband and the reference electrode is connected to the signal acquisition and transmission unit through a hot-swappable design to ensure that all connections are stable and reliable.

[0151] 2. System startup;

[0152] (1) The host computer system issues a collection command: The user operates on the host computer system and issues a command to start collection.

[0153] (2) Base station system analysis instructions: After receiving the acquisition instructions from the host computer system, the base station system analyzes them and controls the high-density EEG and surface EMG signal acquisition system through the wireless network to prepare for work.

[0154] 3. Signal acquisition and transmission;

[0155] (1) Signal acquisition: The high-density EEG and surface EMG electrode arrays begin to collect signals and transmit them to the signal acquisition and transmission unit.

[0156] (2) Data packaging and transmission: The wireless transmission module in the signal acquisition and transmission unit synchronously packages the HD-sEMG signal, HD-EEG signal and the acceleration data collected by the nine-axis acceleration sensor. The packaged data is transmitted to the base station system through the wireless transmission module.

[0157] 4. Data integration and transmission to the host computer;

[0158] (1) Data integration: After receiving data from multiple signal acquisition and transmission units, the base station system performs integration processing.

[0159] (2) Real-time transmission to the host computer: The base station system transmits the integrated data to the host computer system in real time via the data line.

[0160] 5. Data monitoring and storage;

[0161] (1) Real-time data display: The host computer interface displays the HD-EEG and HD-sEMG data waveforms of each channel in real time, and users can monitor the signal quality in real time.

[0162] (2) Adjustment and monitoring: Users search for ports according to the software prompts and connect to the base station system. Before officially storing data, users open the window monitoring and adjust the wearing of the device by observing the matching degree between the displayed waveform and the action taken. When the waveform shows fluctuations with the action and low amplitude in the resting state, data can be recorded.

[0163] (3) Signal quality assessment and marking: The system automatically assesses the signal quality of each channel and identifies abnormal channels due to poor electrode contact or other problems. If the signal quality of some channels is poor, the system will automatically mark and notify the user so that adjustments can be made or the data of these channels can be removed during subsequent processing.

[0164] 6. Data decoding and analysis;

[0165] (1) Data preprocessing: Preprocess the actual recorded HD-EEG and HD-sEMG signals, including noise removal, filtering, and other steps to improve signal quality.

[0166] (2) Feature extraction: After preprocessing, the data will be subjected to feature extraction, including time-frequency domain features, network attribute features, and motion unit time series.

[0167] Through the above steps, the wireless high-density EEG and EMG hybrid synchronous acquisition and monitoring system can efficiently and accurately acquire, transmit, monitor, store and analyze EEG signals and surface EMG signals.

Claims

1. A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system, characterized in that: The wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system consists of three parts: a high-density EEG and surface EMG signal acquisition system, a base station system and a host computer system; the high-density EEG and surface EMG signal acquisition system is responsible for signal acquisition and preliminary processing, the base station system interacts with the high-density EEG and surface EMG signal acquisition system through wireless transmission, and transmits the processed signal to the host computer system for further analysis and display; 1) The high-density EEG and surface EMG signal acquisition system includes: a high-density EEG electrode array, a high-density EMG electrode array, a reference electrode, and a signal acquisition and transmission unit; 1.1) High-density EEG electrode arrays are placed at specific locations on the scalp to collect EEG signals and record the electrical activity of the brain; 1.2) The high-density EMG electrode array is arranged on the surface of skeletal muscle, and the number of contacts of the high-density EMG electrodes in the high-density EMG electrode array is customized according to needs; 1.3) The reference electrode is designed as a wristband and fixed to the tendons of skeletal muscles of the human body; the reference electrode is connected to the signal acquisition and transmission unit through a lead wire; 1.4) The signal acquisition and transmission unit includes an EEG signal acquisition and transmission unit and a surface electromyography signal acquisition and transmission unit. Each signal acquisition and transmission unit includes a signal acquisition chip, a nine-axis acceleration sensor MPU-9250, a microcontroller STM32, a wireless transmission module, a power control and battery module; the wireless transmission module includes a wireless microcontroller CC3235 and a radio frequency transceiver CC2500; Signal acquisition chip: used to amplify and filter the collected EEG signals and surface EMG signals, and convert the analog signals into high-resolution digital signals through a 24-bit analog-to-digital converter, completing the amplification, filtering and digital processing of 64-channel EEG signals or surface EMG signals; Nine-axis acceleration sensor MPU-9250: used to collect acceleration data of the human body, provide motion-related data support for the synchronization of EEG signals and surface electromyography signals, and provide a data basis for monitoring posture and motion trajectory; Microcontroller STM32: used to integrate the EEG and surface electromyography data processed by the signal acquisition chip and the acceleration data collected by the nine-axis acceleration sensor MPU-9250, and transmit the integrated data to the wireless microcontroller CC3235; Wireless microcontroller CC3235: communicates with the base station system in real time through the RF wireless circuit, and is used to synchronously package and send the integrated data from the microcontroller STM32 to the base station system. At the same time, it sends the synchronization control instructions sent by the base station system to the microcontroller STM32 to control the start and stop of signal acquisition and time synchronization of each signal acquisition and transmission unit; RF transceiver CC2500: used to receive the synchronization control instructions of the base station system and send the synchronization control instructions of the base station system to the wireless microcontroller CC3235; The power control and battery module includes a power control unit and a battery unit: The power control unit adopts a low-voltage difference linear voltage regulator circuit design, which ensures the stability of the power supply voltage by adjusting the linear output of the P-MOS tube and minimizes power frequency interference; the battery unit uses a lithium-ion battery to power the entire signal acquisition and transmission unit, which can support the signal acquisition and transmission unit to work continuously for more than four hours and the standby time exceeds ten hours, ensuring the smooth progress of long-term experiments. It can also be charged through the power management and charging module of the base station system; 2) The base station system includes a synchronization control module, a wireless routing module, a power management and charging module, and a host computer communication network port; 2.1) The synchronization control module includes a microcontroller MCU, a radio frequency transceiver CC2500 and an external synchronization trigger unit; the synchronization control module periodically sends synchronization control signals to all signal acquisition units to ensure that the sampling clocks of each acquisition unit are accurately synchronized; the synchronization control module can also perform synchronous acquisition with external devices to ensure the timing alignment of multi-modal data; Microcontroller MCU: used for real-time control of synchronous control signal transmission; RF transceiver CC2500: used to receive the synchronization control signal of the microcontroller MCU and transmit the synchronization control signal to the signal acquisition and transmission unit, and perform synchronization calibration of each signal acquisition and transmission unit; External synchronous trigger unit: It has a synchronous input interface and a synchronous output interface. The synchronous input interface is connected to an external device through a connector, receives input signals from the external device and transmits them to the microcontroller MCU, thereby controlling the start of a high-density EEG and surface electromyography signal acquisition system, and performs synchronous acquisition in cooperation with external devices; the synchronous output interface is connected to an external device, receives synchronous control signals from the microcontroller MCU, and controls the start of an external device for synchronous acquisition; the external device includes a motion capture system or other biological signal equipment; 2.2) The wireless routing module is used to receive data sent by the wireless microcontroller CC3235 of the signal acquisition and transmission unit, and transmit the data to the host computer system through a wired network, supporting the synchronous acquisition and transmission of multi-channel EEG signals and surface electromyography signals; 2.3) The power management and charging module provides power for the base station system and provides remote charging function for the signal acquisition and transmission unit. It ensures long-term stable operation of the equipment by connecting to an AC power source or a lithium battery. The power management and charging module also ensures power stability to prevent data loss or equipment damage caused by overload and unstable power supply. 3) The host computer system includes a data preprocessing and display module, a high-density EEG and EMG decoding algorithm module, and a computer program for integrating data storage, processing, analysis and display based on the EEG and surface EMG data transmitted by the received base station system, so as to monitor and analyze the collected EEG and surface EMG data in real time; 3.1) The data preprocessing and display module is used to filter and perform noise reduction preprocessing on the collected EEG signals and surface electromyography signals and to display the data: the EEG signals are subjected to noise reduction processing to remove electrooculogram and electromyography artifacts; the surface electromyography signals are subjected to bandpass filtering to remove low-frequency artifacts and high-frequency noise, and obtain target surface electromyography signals within the range of 20-500Hz; the preprocessed signal data can be simultaneously displayed on the user interface and stored in a designated path at a specified time according to user needs; 3.2) The high-density EEG and EMG decoding algorithm module is used to decode the preprocessed EEG signals and surface EMG signals; signal decoding includes signal time-frequency domain feature extraction, motor nerve signal decoding, EEG network, EMG network and EEG and EMG coupling network feature analysis; the data after signal decoding includes EEG and surface EMG time-frequency domain features, network attribute features and motor unit time series spatiotemporal features related to the action or task; using the data after signal decoding, and selecting or fusing multimodal features as data input, based on support vector machine, convolutional neural network or long short-term memory network machine learning or deep learning algorithm, movement intention recognition, neuromuscular function evaluation and mechanical control are realized.

2. A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system according to claim 1, characterized in that: Signal time-frequency domain feature extraction is to extract time-frequency domain features of preprocessed EEG signals and surface electromyography signals: A. Signal time domain feature extraction; The time domain feature parameters are directly calculated based on the preprocessed signal. The time domain features include the following: Mean: the average level of the signal; Standard deviation: the degree of dispersion of the signal; Peak-to-peak value: the amplitude range of the signal; RMS value: the time domain energy of the signal; Integrated EMG value: the total electrical activity of muscle contraction; B. Signal frequency domain feature extraction; The frequency domain features of the signal are extracted based on Fourier transform or wavelet transform. The frequency domain features include the following: Power spectral density: the energy distribution of a signal at different frequencies; Median frequency: the middle value of the signal spectrum energy distribution; Average frequency: the arithmetic mean of the instantaneous frequency of the signal; Average power frequency: The power-weighted average of each frequency component of the signal power spectral density.

3. A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system according to claim 2, characterized in that: Motor nerve signal decoding is to further extract the spatiotemporal characteristics of the motor unit time series based on the preprocessed surface electromyographic signal based on the blind source separation algorithm and cluster analysis algorithm: A. Signal expansion; First, the surface electromyography signals of each channel after preprocessing are centered, that is, the average value of each channel is subtracted, and then the covariance matrix of the surface electromyography channel data after centering is calculated, and the covariance matrix is ​​decomposed by eigenvalue. The eigenvectors obtained by decomposition constitute a new feature space, and the signals are projected into the new feature space to eliminate the correlation between the signals. B. Independent source signal extraction; Recover independent source signals from the surface electromyographic signals after centering and decorrelation processing; use the FastICA algorithm to iteratively solve the mixing matrix based on maximizing non-Gaussianity, and further restore the collected surface electromyographic signals to the action potential sequence of the motor unit through the solved mixing matrix as the source signal, filter and decorrelate the restored source signals, and further use the cluster analysis algorithm to combine and classify the extracted source signals; C. Motion unit feature extraction; Based on the A and B processes, the spatiotemporal characteristics of the motor unit time series are extracted to obtain the number of motor units and the spatiotemporal characteristics of the firing rate of the surface electromyographic signal, identify the activation time and activation intensity of different muscles during the movement, and provide detailed information on each muscle activity unit.

4. A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system according to claim 3, characterized in that: The characteristic analysis of EEG network, EMG network and EEG-M coupled network is to obtain the network attribute characteristics based on the coherence analysis or causal analysis of the preprocessed EEG signal and surface EMG signal: A. Coherence or causality analysis; Using coherence analysis or causal relationship analysis on the preprocessed EEG signals and surface electromyography signals, calculating the coherence values ​​or causal relationship measurement values ​​between EEG channels, between electromyography channels, and between EEG and electromyography channels; B. Network construction; Based on the calculated coherence value or causal relationship measurement value, an EEG network, an EMG network and an EEG-MY coupling network are constructed. The network consists of many nodes and edges connecting the nodes. Each signal channel represents a node in the network, and the connection between channels represents an edge in the network. The size of the coherence value or causal relationship measurement value represents the connection strength of the edge in the network. The larger the coherence value or causal relationship measurement value, the closer the connection between the nodes, that is, the stronger the coupling between the channel signals. C. Network feature extraction; According to the constructed EEG network, EMG network and EEG-MY coupling network, the attribute characteristics of the network are calculated. The attribute characteristics include the following: Node degree: the number of edges connecting a node to other nodes; Node clustering coefficient: the proportion of the triangular network around a single node; Global clustering coefficient: the average value of clustering coefficients of all nodes in the network; Characteristic path length: the average shortest path length between all pairs of nodes in the network; Global efficiency: the inverse of the average shortest path length.

5. A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system according to claim 1, characterized in that: The high-density EMG electrode supports a maximum of 640 contacts, and the contact distribution is optimized according to the muscle morphology. The high-density EMG electrode array is made of flexible material with good air permeability and strong water absorption, which fits well with the skin to ensure signal stability.

6. A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system according to claim 1, characterized in that: The interface of the reference electrode adopts a hot-swappable design; the reference electrode wristband conducts electricity by being soaked in conductive liquid, and uses soft fabric with good air permeability and strong water absorption to ensure comfort for long-term wearing. The multi-button design on the reference electrode wristband allows multiple leads to be connected to the same wristband.

7. A wireless high-density EEG and EMG hybrid synchronous acquisition monitoring system according to claim 1, characterized in that: The wireless routing module can simultaneously transmit data with 10 signal acquisition and transmission units, that is, simultaneously transmit 640-channel signals and 60-channel acceleration signals; The wireless routing module supports the remote sending of control commands to the high-density EEG and surface EMG signal acquisition system, adjusts the settings of the signal acquisition and transmission unit and modifies the system parameters, and can meet the transmission bandwidth requirement of 300KSps peak value; it can also receive control instructions issued by the host computer system; it adopts the Modbus communication protocol in the form of RS485 and the full-duplex working mode to ensure the flexibility of data exchange.

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