Integrated electroencephalogram acquisition system based on human-computer interaction

Abstract

An integrated electroencephalogram acquisition system based on man-machine interaction comprises a signal acquisition circuit, an anti-static isolation circuit, a pre-filter circuit, an ADS1299 chip, a microcontroller and a communication module, a signal acquisition electrode is connected with the ADS1299 chip through the anti-static isolation circuit and the pre-filter circuit, the ADS1299 chip is connected with the microcontroller through an SPI communication bus, the microcontroller adopts an STM32F405RGT6, and the communication module is connected with the microcontroller through an SPI communication bus. A five-order IIR band-pass filtering module and a notch filtering module are integrated in the microcontroller, the microcontroller is connected with a communication module, and the microcontroller is in wired communication with an upper computer through a serial port or a USB module and can also be in wireless communication with the upper computer through an ESP32 module. The electroencephalogram signal acquisition device has the advantages of being high in quality of acquired electroencephalogram signals, good in anti-interference performance, low in cost, small in size, low in noise, low in power consumption and the like, capable of achieving long-time continuous high-precision acquisition of the electroencephalogram signals and suitable for being popularized in the field of man-machine interaction.

Description

Technical Field

[0001] The utility model relates to a multi-channel electroencephalogram signal acquisition system, which is applicable to the technical field of electroencephalogram signal acquisition. Background Art

[0002] In the healthcare field, electroencephalography (EEG) is widely used to diagnose and treat brain diseases, such as epilepsy, Alzheimer's disease, Parkinson's disease, etc. In the field of human-computer interaction, EEG is used to identify specific brain wave patterns to control computers, prostheses, or other external devices. Although research on EEG has been around for a long time, there are still many technical challenges. For example, hair, sebum, and other external interfering substances may affect the quality of EEG signals, and differences in EEG signals between different people may also affect their reliability and accuracy. Therefore, further research and development of EEG technology remains an active research area.

[0003] Although the traditional EEG acquisition system can meet the requirements of medical treatment and research in terms of acquisition accuracy, its large size, lack of mobility, inconvenient operation, high price and other shortcomings will reduce the practicality of brain-computer interface technology. Therefore, few brain-computer interface devices are oriented to basic human-computer interaction, which to a certain extent limits the popularity of brain-computer interface technology and indirectly affects the future development of brain-computer interface technology. Utility Model Content

[0004] The purpose of the utility model is to provide an integrated EEG acquisition system based on human-computer interaction in order to solve the above-mentioned problems. The system has the characteristics of low cost, small size, low noise, low power consumption, etc., and can realize long-term continuous acquisition of high-precision EEG signals. It is a multi-channel EEG signal acquisition system suitable for popular use in the field of human-computer interaction.

[0005] The utility model is realized through the following technical solutions.

[0006] The utility model discloses an integrated EEG acquisition system based on human-computer interaction, which comprises a signal acquisition circuit, an anti-static isolation circuit, a pre-filter circuit, an ADS1299 chip, a microcontroller and a communication module, which are connected in sequence.

[0007] The signal acquisition circuit is worn on the user's head to collect the wearer's EEG signals.

[0008] Anti-static isolation circuit, used to protect the signal acquisition chip from the influence of electrostatic discharge.

[0009] The pre-filter circuit is used to process the signal collected by the signal collection circuit and send it to the ADS1299 chip after pre-filtering.

[0010] ADS1299 chip, used to convert the EEG signals collected by the signal collection circuit into discrete digital signal sequences close to the original data, amplify and process the signals, and send the processed EEG signals;

[0011] The microcontroller adopts the STM32F405RGT6 chip, which is used to receive the EEG signals processed by the analog front end, perform data processing on the read EEG signals, and send the processed data at the same time.

[0012] The communication module uses serial port or USB wired communication, or ESP32 is connected to the microcontroller in a wireless communication manner, to establish a communication connection between the microcontroller and the host computer.

[0013] The host computer is used to receive the data sent by the wireless communication module and analyze, process and display the data.

[0014] Preferably, the pre-filter circuit is a low-pass filter circuit.

[0015] The signal acquisition electrode is connected to an ADS1299 chip via an anti-static isolation circuit and a pre-filtering circuit, the ADS1299 chip is connected to a microcontroller via an SPI communication bus, the microcontroller adopts STM32F405RGT6, a 5th-order IIR bandpass filter module and a notch filter module are integrated inside the microcontroller, a communication module is connected to the microcontroller, the microcontroller performs wired communication with a host computer via a serial port or a USB module, and can also perform wireless communication with the host computer via an ESP32 module.

[0016] The working principle of the utility model is as follows.

[0017] Signal collection electrode: Put the electrode cap on the user's head, moisten the cotton swab with 3g / 100ml electrolyte saline, and collect the user's EEG signal.

[0018] Anti-static isolation circuit, the front end of the ADS1299 chip is connected to a TPD4E1B06DCK chip for protecting the high-speed data lines in the circuit from interference from electrostatic discharge (ESD).

[0019] The pre-filter circuit is used to send the EEG signal collected by the signal collection electrode to the ADS1299 chip after pre-filtering.

[0020] The ADS1299 chip is used to amplify the EEG signals collected by the signal collection electrodes and convert them into discrete digital signal sequences that are infinitely close to the original data.

[0021] The ADS1299 chip transmits the converted digital quantity to the microcontroller via SPI transmission, and the data is temporarily stored in the microcontroller.

[0022] The microcontroller, STM32F405RGT6, integrates a 5th-order IIR filtering algorithm to filter the discrete digital signal sequence generated by the ADS1299 chip.

[0023] The communication module is connected to the microcontroller by a serial port or USB, and is used for wireless communication between the microcontroller and the host computer, or is connected to the microcontroller by wireless communication using ESP32, and is used for wireless communication between the microcontroller and the host computer.

[0024] The microcontroller is connected with an impedance value for detecting the contact impedance between the signal acquisition electrode and the user's scalp. The impedance detection module has an impedance detection auxiliary electrode, which is connected to the bias driver (Bias driver) of the ADS1299 chip through a resistor and then grounded; the signal acquisition electrode is connected to the input end (LeadOff) of the ADS1299 chip through a resistor circuit.

[0025] The utility model has an OLED display module connected to a microcontroller, and the prompt module comprises a battery power display, an electroencephalogram signal connection quality prompt, and an ADS1299 working mode display.

[0026] The beneficial effects of the utility model are as follows: the utility model makes the acquired EEG signal of higher quality and better anti-interference performance through dual filtering of hardware and software. The utility model realizes the miniaturization of the EEG signal acquisition system by adopting a highly integrated and high-performance integrated analog front end to replace the original analog amplifier circuit, while maintaining the relative stability and low energy consumption of the system performance. The utility model has the advantages of extremely high input impedance, extremely low input reference noise, adjustable sampling rate, extremely high common mode rejection ratio, programmable gain, extremely low input bias current, extremely low power consumption, and ultra-long standby. The utility model obtains the impedance value of the contact impedance through the impedance detection module, judges the signal quality of the connection between the system and the subject's scalp according to the impedance value of the contact impedance, automatically reminds when the device is low on power, and can automatically detect the lead line disconnection, which has a better human-computer interaction experience. The EEG signal acquisition system of the utility model is equipped with a serial port interface, a USB interface and a wireless communication module at the same time, which can meet different development needs, and will not lose information during high-speed transmission, and has higher communication efficiency and diversified communication methods.

[0027] Through internal parameter testing, the device has the characteristics of low cost, small size, low noise, low power consumption, etc. It can realize long-term continuous and high-precision collection of EEG signals, and is suitable for widespread popularization in the field of human-computer interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the overall structure of the embodiment.

[0029] Figure 2 This is the overall hardware block diagram of the EEG signal acquisition device of the embodiment.

[0030] Figure 3 Schematic diagram of the power supply system in the embodiment.

[0031] Figure 4 It is the corresponding physical picture in the embodiment.

[0032] Figure 5 It is a flow chart of STM32 software program in the embodiment.

[0033] Figure 6 It is a software block diagram of the host computer in the embodiment.

[0034] Figure 7 It is an adaptive denoising module in the embodiment. DETAILED DESCRIPTION

[0035] The following is a further detailed description of the specific implementation of the present invention in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0036] The overall framework of the integrated EEG acquisition system based on human-computer interaction of the utility model is as follows: First, after the subject is connected to the acquisition device through electrodes and lead wires, the EEG signal is transmitted to the ADS1299 chip through the anti-static isolation circuit and the pre-filter circuit. The STM32F4 chip communicates with the ADS1299 chip through the SPI protocol. At the same time, the 5th-order IIR filter algorithm integrated in the STM32F4 chip filters the data. Afterwards, the STM32F4 chip can be directly transmitted to the PC through the universal serial port to USB chip CH340E or the serial bus (Universal Serial Bus, USB), or it can be transmitted wirelessly through the ESP32 module. Then, after the PC receives the signal, it can use the FIR filter algorithm for bandpass filtering and notch filtering and extract and classify data features through a lightweight network model based on an adaptive denoising module. Finally, the processed data is displayed by the host computer software designed by QT.

[0037] like Figure 2 As shown in the figure, the overall hardware device is powered by an external 5V-2A power supply through a DC-DC power isolation module to power all internal chips and external devices. At the same time, in order to meet the safety requirements of medical equipment, the EEG signal acquisition device has a floating isolation design in hardware. In addition to DC-DC power isolation, the USB isolation chip Adum4160 is used to isolate the signal.

[0038] like Figure 3 As shown in the power system block diagram, ADS1299 adopts a multi-power supply design, in which TPS73225 and TPS72325 chips output 2.5V and -2.5V voltages for dual analog power supply, respectively. In addition, an additional LP2983-33 chip is required to provide 3.3V voltage for digital power supply. At the same time, the HT7233 chip is used to provide 3.3V voltage for digital power supply to the STM32F4 chip, CH340E chip and other modules. It should be noted that in addition to using an external 5V power supply to power the input end, the USB isolation chip Adum4160 also requires an additional 3.3V voltage to power its output end.

[0039] Circuit board physical Figure 4 As shown, the lead wire is connected from the subject to the acquisition interface and then passes through the anti-static isolation circuit and then enters the analog pre-filter circuit.

[0040] The filtered EEG signal is then collected into the A / D chip after passing through the differential input port of the ADS1299 chip, and the data is processed internally. Then the EEG signal is converted into a discrete digital signal sequence that is infinitely close to the original data.

[0041] The ADS1299 chip is connected to the STM32F4 chip via SPI for preliminary data processing. At the same time, the STM32F4 communicates with the PC host via a CH340 USB to TTL chip or a USB interface, or communicates with the PC host wirelessly via ESP32. In addition, the STM32F4 can store the collected EEG signals to a local SD card. The power supply is located in the lower left corner of the EEG signal acquisition interface. It should be noted that in order to reduce the ripple of the power supply voltage, a linear voltage regulator chip is used to provide a stable operating voltage.

[0042] The microcontroller processes the digital data transmitted by the ADS1299 chip: a 5th-order IIR filter algorithm is integrated inside the microcontroller for filtering.

[0043] In this embodiment, the microcontroller is connected to an impedance detection module for detecting the impedance value of the contact impedance between the signal acquisition electrode and the user's scalp. The impedance detection module has an impedance detection auxiliary electrode, which is connected to the Bias driver of the ADS1299 chip through a resistor and then grounded; the signal acquisition electrode is connected to the LeadOff of the ADS1299 chip through a resistor circuit.

[0044] The overall software program flow chart is as follows Figure 5As shown, this embodiment uses the STM32F405RGT6 chip for MCU program development. The functions implemented by the program mainly include the following parts: configuring the ADS1299 chip through the SPI interface and some GPIOs; reading the EEG signal data returned by the ADS1299 chip through the SPI interface; sending the EEG signal data to the PC through UART or USB, and also sending the EEG signal data to the PC through ESP32 in a wireless communication manner.

[0045] The PC side performs feature extraction training on the received signal data through a lightweight network model based on an adaptive denoising module, classifies and organizes the original disorganized data, and displays it through a host computer visualization interface.

[0046] Figure 6 This is the software block diagram of the host computer. The software mainly completes the functions of data reception and storage, signal processing, and signal and result display. In the current industrial control system, serial port and USB communication are widely used. The former is simple, reliable and stable, while the latter has a high transmission rate. Theoretically, the maximum communication rate of the serial port can reach 3Mbps, and the USB can reach 12Mbps in full-speed mode. In practical applications, since the human body is coupled with the radiation noise of external electronic devices and other bioelectric signals of the human body, it is necessary to use a bandpass filter and a notch filter to filter out the electrocardiogram, electrooculogram, myoelectric noise and 50Hz power frequency noise generated by the human body. Further, the FFT algorithm and short-time Fourier transform algorithm are used to convert the filtered EEG signal to obtain the frequency domain representation and time-frequency domain representation of the signal.

[0047] In order to obtain relatively pure EEG signal features, this embodiment draws on the denoising algorithm in modulation signal recognition to perform nonlinear transformation on the preprocessed EEG signal.

[0048] like Figure 7 As shown, this embodiment constructs an adaptive denoising module that can automatically filter out noise in EEG signals. C Indicates the number of feature channels for input and output, L represents the characteristic length, G and k They represent the number of groups and the size of the convolution kernel of one-dimensional convolution respectively, and ReLu and Sigmoid represent nonlinear activation functions.

[0049] This embodiment uses a gradient descent algorithm to automatically update the parameters of the one-dimensional convolution layer, the fully connected layer, and the batch normalization in the adaptive denoising module without manual intervention.

[0050] The above implementation modes are only used to illustrate the present invention, but not to limit the present invention. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The scope of patent protection of the present invention should be limited by the claims.

Claims

1. An integrated EEG acquisition system based on human-computer interaction, characterized in that: It includes a signal acquisition circuit, an anti-static isolation circuit, a pre-filter circuit, an ADS1299 chip, a microcontroller and a communication module, which are connected in sequence; The signal acquisition circuit is worn on the user's head to collect the wearer's EEG signals; Anti-static isolation circuit, used to protect the signal acquisition chip from the influence of electrostatic discharge; A pre-filter circuit is used to process the signal collected by the signal collection circuit and send it to the ADS1299 chip after pre-filtering; ADS1299 chip, used to convert the EEG signals collected by the signal collection circuit into discrete digital signal sequences close to the original data, amplify and process the signals, and send the processed EEG signals; A microcontroller, wherein the microcontroller adopts an STM32F405RGT6 chip, and is used to receive the EEG signal processed by the analog front end, perform data processing on the read EEG signal, and send the processed data; The communication module uses serial port or USB wired communication, or ESP32 is connected to the microcontroller in a wireless communication manner, to establish a communication connection between the microcontroller and the host computer; The host computer is used to receive the data sent by the wireless communication module and analyze, process and display the data.

2. The integrated EEG acquisition system based on human-computer interaction according to claim 1, characterized in that: The TPD4E1B06DCK chip in the anti-static isolation circuit is connected to the front end of the ADS1299 chip to protect the circuit from electrostatic discharge.

3. The integrated EEG acquisition system based on human-computer interaction according to claim 1 is characterized in that: The pre-filter circuit is a low-pass filter circuit.

4. The integrated EEG acquisition system based on human-computer interaction according to claim 1 is characterized in that: The microcontroller and the ADS1299 chip transmit information via the SPI protocol.

5. The integrated EEG acquisition system based on human-computer interaction according to claim 1, characterized in that: The microcontroller is connected to the impedance detection module of the ADS1299 chip.

6. The integrated EEG acquisition system based on human-computer interaction according to claim 1, characterized in that: The host computer displays the processed signal through a visual interface.

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