Electroencephalogram signal acquisition system and method, storage medium and electronic equipment

Abstract

The invention discloses an electroencephalogram signal acquisition system and method, a storage medium and electronic equipment, and relates to the technical field of data processing.The system comprises an analog front end, a microcontroller and a wireless module; the analog front end is used for collecting an EEG analog signal and processing the EEG analog signal to obtain an EEG digital signal; the microcontroller is used for converting the EEG digital signal into electroencephalogram data; and the wireless module is used for transmitting the electroencephalogram data to an upper computer through the Wi-Fi module. Compared with the prior art, the system has the advantages that the analog front end 11 can be used for collecting EEG analog signals, converting the EEG analog signals into EEG data signals and transmitting the EEG data signals to the microcontroller 12, the microcontroller 12 is used for processing the EEG data signals and generating electroencephalogram data, and finally the wireless module 13 is used for sending the electroencephalogram data to the upper computer. The activity range of the user is expanded, and the convenience of electroencephalogram signal collection is improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to an electroencephalogram (EEG) signal acquisition system, method, storage medium, and electronic device. Background Art

[0002] Collecting EEG signals helps researchers better understand the neural activities and functions of the brain, and has extensive applications in the fields of neuroscience, psychology, medicine, artificial intelligence, etc. For example, evaluating brain health status, diagnosing certain neurological diseases, etc.

[0003] Currently, in the related art, the acquisition of EEG signals is mainly transmitted through wired means. However, this method limits the user's range of activities and may even cause problems such as wire entanglement, affecting the user's safety. Summary of the Invention

[0004] In view of this, this application provides an EEG signal acquisition system, method, storage medium, and electronic device, mainly aiming to improve the technical problem that in the current existing technology, the acquisition of EEG signals is mainly transmitted through wired means, but this method limits the user's range of activities and may even cause problems such as wire entanglement, affecting the user's safety.

[0005] In the first aspect, this application provides an EEG signal acquisition system, including: an analog front end, a microcontroller, and a wireless module;

[0006] The analog front end is used to collect an analog electroencephalogram (EEG) signal and process the EEG analog signal to obtain an EEG digital signal;

[0007] The microcontroller is used to convert the EEG digital signal into EEG data;

[0008] The wireless module is used to transmit the EEG data to a host computer through a Wi-Fi module.

[0009] In the second aspect, this application provides an EEG signal acquisition method, including:

[0010] Invoking the analog front end to collect an EEG analog signal and processing the EEG analog signal to obtain an EEG digital signal;

[0011] Invoking the microcontroller to convert the EEG digital signal into EEG data;

[0012] Invoking the Wi-Fi module in the wireless module to transmit the EEG data to a host computer.

[0013] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the electroencephalogram signal acquisition method of the second aspect is implemented.

[0014] In a fourth aspect, the present application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the electroencephalogram signal acquisition method of the second aspect is implemented.

[0015] In a fifth aspect, the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the electroencephalogram signal acquisition method of the second aspect is implemented.

[0016] By means of the above technical solutions, the present application provides an electroencephalogram signal acquisition system, method, storage medium, and electronic device. Among them, the electroencephalogram signal acquisition system includes: an analog front end, a microcontroller, and a wireless module; the analog front end is used to collect the electroencephalogram (EEG) analog signal and process the EEG analog signal to obtain an EEG digital signal; the microcontroller is used to convert the EEG digital signal into electroencephalogram data; the wireless module is used to transmit the electroencephalogram data to a host computer through a Wi-Fi module. Compared with the current existing technologies, the present application can use the analog front end 11 to collect the EEG analog signal, convert the EEG analog signal into an EEG data signal, and then transmit it to the microcontroller 12. The microcontroller 12 is used to process the EEG data signal to generate electroencephalogram data. Finally, the wireless module 13 is used to send the electroencephalogram data to the host computer. By this way of wirelessly transmitting electroencephalogram data, problems such as wire entanglement caused by wired transmission and affecting the safety of users are avoided, the activity range of users is expanded, and the convenience of electroencephalogram signal acquisition is improved.

[0017] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specifically illustrates the specific implementation manners of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing the embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 shows a schematic structural diagram of an electroencephalogram (EEG) signal acquisition system provided by an embodiment of the present application;

[0021] Figure 2 shows a schematic flow diagram of an electroencephalogram (EEG) signal acquisition method provided by an embodiment of the present application. Detailed implementation manners

[0022] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0023] In order to improve the technical problem that the acquisition of EEG signals in the current prior art is mainly transmitted by a wired method, but this method limits the range of activities of users and may even cause problems such as wire entanglement, affecting the safety of users. This embodiment provides an EEG signal acquisition system, as Figure 1 shown, including: an analog front end 11, a microcontroller 12, and a wireless module 13;

[0024] The analog front end 11 is used to collect EEG analog signals and process the EEG analog signals to obtain EEG digital signals; the microcontroller 12 is used to convert the EEG digital signals into EEG data; the wireless module 13 is used to transmit the EEG data to a host computer through a Wi-Fi module.

[0025] In a specific application scenario, EEG signals are usually weak sine waves with amplitudes between 0.5 - 200 uV. Therefore, high amplification is required for EEG signal acquisition. When the brain is stimulated by external factors, thinking, or the physical state changes, the frequency and amplitude of brain waves also change. When stimulated by external factors, the human brain will quickly respond within 1 second and generate complex brain wave signals. Different emotional states of brain electrical activities can be analyzed through EEG signals, or direct interaction between the human brain and a computer can be achieved.

[0026] In some embodiments, the analog front end 11 can be used to preprocess the EEG analog signals from the sensors for subsequent digital processing. In an EEG signal acquisition system, weak EEG signals can be amplified, filtered, and converted into EEG digital signals suitable for digital processing to ensure the quality and stability of the acquired signals. The microcontroller 12 can be used to preprocess the received EEG digital signals (such as removing noise (such as electromyogram interference, power supply interference), baseline correction, filtering, etc.), extract features (such as extracting frequency-domain features (such as power spectral density), time-domain features (such as average amplitude), time-frequency domain features (such as wavelet transform)), classify and recognize (such as using machine learning algorithms (such as support vector machines, artificial neural networks, convolutional neural networks) to classify and recognize EEG signals), etc., to obtain the processed EEG data. The wireless module 13 can be used to transmit the EEG data to the host computer through a chip with a Wi-Fi module for display of the EEG data, facilitating the analysis of EEG activities and improving the transmission efficiency and portability of EEG signal acquisition.

[0027] Compared with the current existing technologies, in this embodiment, the analog front end 11 can be used to collect EEG analog signals, convert the EEG analog signals into EEG data signals, and then transmit them to the microcontroller 12. The microcontroller 12 is used to process the EEG data signals to generate EEG data. Finally, the wireless module 13 is used to send the EEG data to the host computer. Through this wireless transmission method, problems such as wire entanglement caused by wired transmission, which affect the safety of users, are avoided, the activity range of users is expanded, and the convenience of EEG signal acquisition is improved.

[0028] Optionally, the output end of the analog front end 11 can be connected to the input end of the microcontroller 12, and the output end of the microcontroller 12 can be connected to the input end of the wireless module 13.

[0029] As a possible implementation, a standard EEG cap can be used to collect EEG analog signals. The electrodes placed on the scalp record the electrical signals of the brain, which reflect the electrical activities of brain neurons. Then, the analog front end 11 is used to collect the EEG analog signals, perform amplification, filtering, and conversion processing to obtain EEG digital signals and send them to the microcontroller 12. Then, the microcontroller 12 can receive the EEG digital signals at a preset sampling rate, and then encapsulate the EEG digital signals into EEG data convenient for transmission, and use the wireless module 13 to send them frame by frame to the host computer. Exemplarily, the electrodes of the standard EEG cap can be connected to the input end of the analog front end 11, and the output end of the analog front end 11 is connected to the input end of the microcontroller 12 to transmit the EEG digital signals. The output end of the microcontroller 12 can be connected to the input end of the wireless module 13 to transmit the EEG data.

[0030] Optionally, the wireless module 13 may include a Node Micro-Controller Unit (NodeMCU); the NodeMCU is used to receive the EEG data sent by the microcontroller 12 frame by frame and transmit the EEG data to the host computer frame by frame through the Wi-Fi module of ESP8266.

[0031] Exemplarily, the NodeMCU can meet the requirements of 8-channel data transmission, so as to facilitate the wireless transmission of 8-channel EEG data, with a relatively high transmission speed, thereby improving the transmission efficiency of the EEG data. Specifically, the NodeMCU is an open-source IoT (Internet of Things) development board encapsulated on the basis of ESP8266-12E. The NodeMCU development kit / board consists of an ESP8266 Wi-Fi enabled chip. The ESP8266 is a low-cost Wi-Fi chip developed through the Transmission Control Protocol / Internet Protocol (TCP / IP) protocol of Espressif Systems. The NodeMCU development kit has pins similar to analog (i.e., A0) and digital (D0-D8) pins of Arduino on its circuit board. The NodeMCU development board has Wi-Fi function, analog pins, digital pins and serial communication protocols, and supports serial communication protocols such as Universal Asynchronous Receiver / Transmitter (UART), Serial Peripheral Interface (SPI), I2C, etc.

[0032] In some embodiments, the NodeMCU can be used to undertake the work of SPI communication and Wi-Fi communication, and the specific functions are as follows:

[0033] SPI communication: The NodeMCU can receive the EEG data from the embedded single-chip microcomputer STM32 through the SPI interface, frame by frame, and the size of each frame of data can be 2407 bytes. When the PC6 pin of STM32 is pulled high (the high-level duration of the PC6 pin is 50 us), it can indicate that a frame of data is ready, and the NodeMCU can be notified to start the transmission. When the NodeMCU detects the high-level signal of D1 (connected to the PC6 of STM32), it can start to receive the EEG data through SPI. Whenever a frame of EEG data is received, the received frame of EEG data can be stored in an array (such as the Str array).

[0034] Wi-Fi Communication: The NodeMCU can connect to Wi-Fi and send a frame of EEG data to the cloud server via a POST request in the Hypertext Transfer Protocol (HTTP). This process is repeated to achieve the transmission of EEG data frame by frame. Through this wireless transmission method, problems such as wire entanglement caused by wired transmission and impacts on user safety are avoided, the user's activity range is expanded, and the convenience of EEG signal acquisition is improved.

[0035] Optionally, the analog front end 11 transmits the EEG digital signal to the microcontroller 12 through the SPI communication protocol; the microcontroller 12 can transmit the EEG data to the wireless module 13 through the SPI communication protocol.

[0036] Specifically, SPI is a high-speed, full-duplex, synchronous communication bus that only occupies four lines on the chip pins, saving space for chip pins and PCB layout. The data transmission rate is relatively high (such as MB / s). SPI has advantages such as fewer signal lines, simple protocol, and high data rate, which provides convenience for the integration work of the EEG signal acquisition system. Correspondingly, the maximum clock frequency of STM32F103ZET6 SPI communication is 18 MHz; for example, standard SPI can use 4 pins for data transmission: MOSI - master device data output, slave device data input.

[0037] Optionally, the analog front end 11 may include an ADS1299 chip; the ADS1299 chip is used to receive the preprocessed EEG analog signal and process the EEG analog signal to obtain the EEG digital signal.

[0038] In some embodiments, the core of the EEG signal acquisition system may be the analog front end 11 for measuring bioelectrical potential, the ADS1299. The integrated simulation front end has all the common features required for EEG applications. Exemplarily, the EEG signal acquisition system can use the ADS1299 device to acquire 8-channel EEG signals. The specific communication protocol is as follows:

[0039] 8-channel ADS1299 (ADS1299×1), with a sampling rate of 500 Hz. The sampled data of each channel is represented by 3 bytes. The amount of data collected at one time is 8×3 = 24 bytes, and the original data volume is 24×500 = 12000 bytes / s (12 KB / s). To adapt to the HTTP protocol, the 3 bytes of each channel can be converted into 6 bytes for transmission. The number of bytes collected in one second is: 500×8×3×2 = 24000 B (24 KB, 24 K bytes). The amount of data sampled at one time is 8×3×2 = 48 B (48 bytes), and the amount of data for 50 samplings is: 50×8×3×2 = 50×48 = 2400 B (2.4 KB, 2.4 K bytes). 50 samplings can be one frame of data. Adding 2 bytes for the frame header, 4 bytes for the frame number, and 1 byte for the checksum (the high bit is discarded) for encapsulation, the electroencephalogram data is obtained. Correspondingly, the size of one frame of data is: 2407 bytes. 10 frames of electroencephalogram data need to be transmitted per second. NodeMCU processes one frame (2407 (3*8*2*50 + 7) bytes) of electroencephalogram in 100 ms. The processing process can include receiving electroencephalogram data through SPI and sending the electroencephalogram data to the cloud server using Wi-Fi.

[0040] Correspondingly, the ADS1299 is a 24-bit Delta-Sigma ADC with 8 low-noise synchronous sampling channels, equipped with a programmable gain amplifier (PGA), reference voltage, and oscillator, integrating the general characteristics required for electroencephalogram (EEG). The input-referred noise of this device is very low, at 1.0 uVPP (70 Hz BW). The power consumption of each channel is 5 mW, the input bias current is 300 pA, the data rate is 250 sps - 16 ksps, C1.0 uVPP (70 Hz BW), the CMMR is -110 dB, the programmable gain is 1, 2, 4, 6, 8, 12, or 24, and the gain value is selected depending on the amplitude of the input signal to amplify the signal to a range suitable for ADS1299 conversion and operate using a single-pole or bipolar power supply. In specific application scenarios, it can be applied to the acquisition process of high-precision multi-channel signal data such as EEG, electrocardiogram (ECG), evoked auditory potential (EAP), bispectral index (BIS) of electroencephalogram, and sleep research monitoring in medical instruments.

[0041] In some embodiments, the internal registers of the ADS1299 enable it to perform its main functions, such as changing the input mode of signals, amplification factor, sampling rate, etc. Before collecting data, the ADS1299 needs to be initialized. The initialization process may include the following steps: First, raise the PWDN pin to power on the chip internally; pull down the RESET pin to reset the chip, and pull down the CS pin to select the SPI interface of the chip; then send the SDATAC command and the WREG command to configure the CONFIG1, CONFIG2, CONFIG3 registers and other registers of each channel and set other functions; finally, perform data sampling, and the ADS1299 waits to be read, and the initialization process ends. The ADS1299 can communicate with the main control module through the SPI interface to achieve synchronous data transmission and reception. The SPI communication of the ADS1299 can adopt a four-wire system, including a clock signal SCLK, a data input line DIN, a data output line DOUT, and a chip select line CS. The ADS1299 can only operate in the slave mode of SPI communication.

[0042] Optionally, the microcontroller 12 may include an STM32F103 main controller; the STM32F103 main controller operates in the SPI master mode and is used to configure the switch state of the input end corresponding to the ADS1299 chip and preset the sampling data.

[0043] In some embodiments, an STM32F103 main controller with a serial peripheral interface may be selected to facilitate the ADS1299 to read and control the Wi-Fi module. Exemplarily, in an electroencephalogram signal acquisition system, the main controller STM32F103ZET6 can communicate with slave devices such as the ADS1299 through SPI. The main controller can operate in the main SPI mode and is mainly responsible for front-end initialization of acquisition, including channel activation, incremental and shift adjustment of each channel, etc.

[0044] Specifically, the STM32F103ZET6 is an embedded microcontroller integrated circuit (IC) in the STM32F1 series. The chip size is 32 bits, the main frequency is 72 MHz, the program storage capacity is 256 KB, the program storage type is FLASH, and the random access memory (RAM) capacity is 48K. The resources of the STM32F103ZET6 include: 64 KB SRAM, 512 KB FLASH, 2 basic timers, 4 general timers, 2 advanced timers, 2 DMA controllers (a total of 12 channels), 3 SPIs, 2 IICs, 5 serial ports, 1 USB, 1 CAN, 3 12-bit ADCs, 1 12-bit DAC, 1 SDIO interface, 1 FSMC interface, and 112 general-purpose IO ports.

[0045] Correspondingly, STM32F103ZET6 and STM32 represent a 32-bit microcontroller with an ARM Cortex-M3 core; F represents the chip sub-series; 103 represents the enhanced series; Z represents the number of pins, where T represents 36 pins, C represents 48 pins, R represents 64 pins, V represents 100 pins, and Z represents 144 pins; E represents the embedded flash memory capacity, where 6 represents 32K bytes of flash memory, 8 represents 64K bytes of Flash, B represents 128K bytes of Flash, C represents 256K bytes of Flash, D represents 384K bytes of flash memory, and E represents 512K bytes of Flash; T represents the package, where H represents the BGA package, T represents the LQFP package, and U represents the VFQFPN package; 6 represents the operating temperature range, where 6 represents -40 - 85°C and 7 represents -40 - 105°C.

[0046] Optionally, the STM32F103 main controller is used to receive the EEG digital signal sent by the ADS1299 chip according to the preset sampling data and encapsulate the EEG digital signal into EEG data for frame-by-frame transmission.

[0047] Optionally, the preset sampling data may include a preset sampling frequency, a preset amplification factor, etc.

[0048] In some embodiments, STM32F103ZET6 operates in SPI master mode, mainly responsible for configuring the on / off of various input terminals of ADS1299, setting the ADS1299 register, the programmable amplifier amplification factor, and the sampling frequency value. Exemplarily, the preset amplification factor of the PGA in this system can be 24, and the preset sampling frequency can be 500Hz. After detecting the DRDY falling edge signal, the microcontroller 12 can start reading the high-resolution EGG digital signal converted by ADS1299. The upload and transmission rate of the EEG data can be calculated according to the following formula:

[0049] Throughput = x × 3 (B / ch) × 8 (bit / B) × (1 / T)

[0050] In the formula, the throughput can refer to the number of data bits transmitted per second (bits / s); T can be a sampling period; x can be the number of leads. The sampling frequency can be 500Hz, so T can be 0.002 and x can be 8. The required upload rate can be calculated as 96000 bit / s.

[0051] In some embodiments, ADS1299 can transmit the sampled EGG digital signal to STM32 through the SPI interface. STM32 processes the data, such as format conversion, and then transmits it to NodeMCU through the SPI interface for further processing. The specific process is as follows:

[0052] Receive data from ADS1299 (8 channels) through SPI communication interrupt, interrupt once every 2 ms (sampling rate 500 Hz), then convert the data from hexadecimal data format to ASCII hexadecimal data form (one byte is converted to two bytes), add a frame header, frame number, and checksum to form a frame of data. After preparing a frame of data, pull out PC6 (data ready pin) for 50 us, wait for NodeMCU to detect it, and then perform SPI transmission;

[0053] Exemplarily, STM32 sends the converted data. One HEX (hexadecimal) byte is converted into two ASCII codes for transmission. The original data of one channel is 3 bytes, and the converted data is 6 bytes. The total amount of data after one conversion is 48 bytes. The length of each frame of data is 2407 bytes, including data of 50 samples. The interval between each frame is 100 ms, and 10 frames of data are sent per second. The communication data volume is 2407×10 = 24.07 KB / s.

[0054] Optionally, the host computer can be used to receive the EEG data sent by the wireless module 13 and display the EEG data.

[0055] In some embodiments, the host computer can establish a Wi-Fi connection with NodeMCU, receive and parse the EEG data sent by NodeMCU frame by frame, and can select the display method of the EEG data according to specific application requirements and user interface design, such as waveform diagram, spectrogram, 3D diagram, etc., to display the changes of EEG signals of each channel, which helps to analyze the characteristics of EEG signals, so as to visualize multi-channel data and facilitate real-time monitoring and analysis of EEG signals.

[0056] Compared with the prior art, this embodiment can use the ADS1299 integrated chip to receive the pre-processed EEG analog signal after low-pass filtering, filter, amplify and perform analog-to-digital conversion on the EEG analog signal, and at the same time send the processed EEG digital signal to the microcontroller 12; then use the STM32F103 microcontroller 12 to receive the EEG digital signal processed by the analog front end 11, read the EEG digital signal and encapsulate it into EEG data and send it to NodeMCU frame by frame; finally use NodeMCU to receive the EEG data sent by the microcontroller 12 frame by frame and transmit the EEG data to the host computer through Wi-Fi communication, effectively avoiding problems such as wire entanglement caused by wired transmission, affecting the safety of users, etc., reducing the restriction on user activities, expanding the user's activity range, improving the convenience of EEG signal acquisition, and the system is small in size and light in weight, facilitating users to carry and understand their physical conditions in real time; through wireless transmission, the EEG data corresponding to the collected EEG signals can be visualized on a public network interface, facilitating users to view the EEG data in real time and improving the real-time performance of EEG signal acquisition and analysis.

[0057] To illustrate the specific implementation processes of the above embodiments, the following application scenarios are given, but are not limited thereto:

[0058] An electroencephalogram (EEG) acquisition system based on ADS1299, STM32, and ESP8266, which is characterized by being simple to use and having high precision, can provide guarantee for further EEG research. Specifically, it includes: an analog front-end that uses an ADS1299 integrated chip to receive EEG signals preprocessed by low-pass filtering, filtering, amplification, and analog-to-digital conversion, and sends the processed EEG signals; a microcontroller 12 using STM32F103 is used to receive the EEG signals processed by the analog front-end 11, process the read EEG signals, and send the processed data; a wireless module 12, which uses NodeMCU to receive the processed data sent by the microcontroller 12 and transmits the data to the host computer through Wi-Fi communication; the host computer is used to receive the data sent by the wireless module 13, analyze, process, and display the data.

[0059] The overall framework of the EEG acquisition system based on ADS1299, STM32, and ESP8266 is as follows: First, 8-channel analog signals are collected using a standard EEG cap, and high-resolution data is obtained using a high-precision 24-bit analog front-end ADS1299. Then, the digital signals are transmitted to the main control module STM32F103ZET6 through the SPI interface. The main control module can control ADS1299 through the SPI bus and transmit digital signals through the NodeMCU SPI interface. NodeMCU then transmits the signals to the host computer through the Wi-Fi module, and the host computer can display and record the EEG signals in real time, providing convenience for further analysis and investigation. The sampling rate of this EEG acquisition system is 500 Hz, and the electrode placement can be based on the international 10-20 system standard. Spontaneous electroencephalograms are collected, and 8-channel electrodes can be connected to positions P3, P4, Pz, O1, Oz, O2, and T7.

[0060] In this way, the EEG acquisition system in this embodiment converts wired transmission into wireless transmission, reduces the restriction on the free movement of users, and the system has a small volume and light weight, which is convenient for users to carry; the EEG data corresponding to the collected EEG signals can be visualized on a public network interface, facilitating users to view the EEG data in real time. From the perspective of doctors, there is a stronger sense of interaction with the electroencephalogram data of patients, and it can be viewed by connecting to the public network; for patients, the EEG signals can be visualized on a public network interface, which helps to better understand their physical conditions.

[0061] To further illustrate the specific implementation process of the method in this embodiment, this embodiment provides an electroencephalogram signal acquisition method, as Figure 2 shown, which can be applied to an electroencephalogram signal acquisition system, and the method includes:

[0062] Step 201: Call the analog front end to collect the electroencephalogram (EEG) analog signal, and process the EEG analog signal to obtain the EEG digital signal.

[0063] Optionally, the analog front end may include an ADS1299 chip; the ADS1299 chip can be called to receive the preprocessed EEG analog signal, and process the EEG analog signal to obtain the EEG digital signal.

[0064] Step 202: Call the microcontroller to convert the EEG digital signal into electroencephalogram data.

[0065] Optionally, the microcontroller may include an STM32F103 main controller; the STM32F103 main controller operates in SPI master mode, which is used to configure the switch state of the corresponding input terminal of the ADS1299 chip and preset the sampling data. The STM32F103 main controller is used to receive the EEG digital signal sent by the ADS1299 chip according to the preset sampling data, and encapsulate the EEG digital signal into electroencephalogram data for frame-by-frame transmission.

[0066] Step 203: Call the Wi-Fi module in the wireless module to transmit the electroencephalogram data to the host computer.

[0067] Optionally, the wireless module may include a NodeMCU; the NodeMCU can be used to receive the electroencephalogram data sent by the microcontroller frame by frame, and transmit the electroencephalogram data to the host computer frame by frame through the Wi-Fi module of the ESP8266.

[0068] Optionally, the host computer can be used to receive the electroencephalogram data sent by the wireless module and display the electroencephalogram data.

[0069] Optionally, the analog front end can transmit the EEG digital signal to the microcontroller through the SPI communication protocol; the microcontroller can transmit the electroencephalogram data to the wireless module through the SPI communication protocol.

[0070] Compared with the current existing technologies, in this embodiment, the analog front end can be used to collect the EEG analog signal, convert the EEG analog signal into an EEG data signal, and then transmit it to the microcontroller. The microcontroller processes the EEG data signal to generate electroencephalogram data. Finally, the Wi-Fi module in the wireless module is used to send the electroencephalogram data to the host computer. Through this wireless transmission method, problems such as wire entanglement caused by wired transmission and affecting the safety of users are avoided, the activity range of users is expanded, and the convenience of electroencephalogram signal acquisition is improved.

[0071] Based on the above as Figure 2The method described above, correspondingly, this embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned method as Figure 2 shown.

[0072] Based on such an understanding, the technical solution of this application can be embodied in the form of a software product, and this software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of this application.

[0073] Based on the above-mentioned method as Figure 2 shown, in order to achieve the above object, this embodiment of the application also provides an electronic device, such as intelligent terminals such as personal computers, servers, laptop computers, smart phones, and intelligent robots. This device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the above-mentioned method as Figure 2 shown.

[0074] Optionally, the above-mentioned physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, and so on. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0075] Those skilled in the art can understand that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine some components, or have different component arrangements.

[0076] The storage medium may further include an operating system and a network communication module. The operating system is a program for managing the hardware and software resources of the above-mentioned physical device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between components inside the storage medium, and communication between other hardware and software in the information processing physical device.

[0077] Based on the above-mentioned method as Figure 2 shown, this embodiment of the application also provides a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the above-mentioned method as Figure 2For the method shown, the method implemented when the computer program is executed by the processor may refer to the various embodiments of the present application and will not be elaborated here.

[0078] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or can also be implemented by hardware. Compared with the current existing technologies, this embodiment can use the ADS1299 integrated chip to receive the preprocessed EEG analog signal after low-pass filtering, and filter, amplify, and perform analog-to-digital conversion on the EEG analog signal, and at the same time send the processed EEG digital signal to the microcontroller 12; then use the STM32F103 microcontroller 12 to receive the EEG digital signal processed by the analog front end 11, read the EEG digital signal and encapsulate it into EEG data and send it frame by frame to the NodeMCU; finally use the NodeMCU to receive the EEG data sent by the microcontroller 12 frame by frame, and transmit the EEG data to the upper computer through Wi-Fi communication, effectively avoiding problems such as wire entanglement caused by wired transmission, affecting the safety of users, etc., reducing the restriction on the activities of users, expanding the activity range of users, improving the convenience of EEG signal acquisition, and the system has a small volume and light weight, which is convenient for users to carry and understand their physical conditions in real time; through wireless transmission, the EEG data corresponding to the collected EEG signal can be visualized on the public network interface, which is convenient for users to view the EEG data in real time and improves the real-time performance of EEG signal acquisition and analysis.

[0079] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0080] The above are only the specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments described herein, but will conform to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An electroencephalogram signal acquisition system, characterized in that, Including: An analog front end, a microcontroller, and a wireless module; The analog front end is used to collect EEG analog signals and process the EEG analog signals to obtain EEG digital signals; The microcontroller is used to convert the EEG digital signals into electroencephalogram data; The wireless module is used to transmit the electroencephalogram data to a host computer through a Wi-Fi module.

2. The system according to claim 1, wherein The wireless module includes a NodeMCU; The NodeMCU is used to receive the electroencephalogram data sent by the microcontroller frame by frame and transmit the electroencephalogram data to the host computer frame by frame through the Wi-Fi module of the ESP8266.

3. The system according to claim 1, wherein The analog front end transmits the EEG digital signals to the microcontroller through an SPI communication protocol; The microcontroller transmits the electroencephalogram data to the wireless module through the SPI communication protocol.

4. The system according to claim 3, wherein The analog front end includes an ADS1299 chip; The ADS1299 chip is used to receive the preprocessed EEG analog signals and process the EEG analog signals to obtain the EEG digital signals.

5. The system according to claim 4, characterized in that, The microcontroller includes an STM32F103 main controller; The STM32F103 main controller operates in SPI master mode and is used to configure the switch states of the corresponding input terminals of the ADS1299 chip and preset sampling data.

6. The system according to claim 5, wherein The STM32F103 main controller is used to receive the EEG digital signals sent by the ADS1299 chip according to the preset sampling data and encapsulate the EEG digital signals into electroencephalogram data for frame-by-frame transmission.

7. The system according to claim 1, characterized in that The host computer is used to receive the electroencephalogram data sent by the wireless module and display the electroencephalogram data.

8. A method for collecting electroencephalogram signals, characterized in that, Applied to the electroencephalogram signal acquisition system according to any one of claims 1 to 7, including: Invoking the analog front end to collect EEG analog signals and processing the EEG analog signals to obtain EEG digital signals; Invoking the microcontroller to convert the EEG digital signals into electroencephalogram data; Invoking the Wi-Fi module in the wireless module to transmit the electroencephalogram data to the host computer.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the method according to claim 8 is implemented.

10. An electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, the method according to claim 8 is implemented.

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