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BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System

Bing Zou, Yubo Zheng, Mu Shen, Yingying Luo, Lei Li, Lin Zhang

TL;DR

BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4 kHz with wireless transmission and displays a better sampling rate than state-of-the-art systems used in many EEG fields, which makes it can be quickly reproduced.

Abstract

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition system's hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front-end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4k Hz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. The data loss is 0 packets and the average maximum delay is only 0.07 s/h. Moreover, as an open source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced. Schematics, source code, and other materials of BEATS are available at https://github.com/buptantEEG/BEATS.

BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System

TL;DR

BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4 kHz with wireless transmission and displays a better sampling rate than state-of-the-art systems used in many EEG fields, which makes it can be quickly reproduced.

Abstract

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition system's hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front-end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4k Hz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. The data loss is 0 packets and the average maximum delay is only 0.07 s/h. Moreover, as an open source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced. Schematics, source code, and other materials of BEATS are available at https://github.com/buptantEEG/BEATS.
Paper Structure (41 sections, 3 equations, 9 figures, 5 tables)

This paper contains 41 sections, 3 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Analog Front End
  3. ADC-Board Design
  4. Analog-to-Digital Converter Module
  5. Analog Input Module
  6. Electrodes Driver Module
  7. Motherboard Design
  8. Power Supply Module
  9. Clock Module
  10. Multiple Devices Configuration
  11. PCB Design and Manufacturing
  12. Microprocessor
  13. ADC Configuration
  14. Underlying Techniques
  15. Interrupt
  16. ...and 26 more sections

Figures (9)

  • Figure 1: The architecture of BEATS. Electrodes are firstly attached to human body according to the electrophysiological signals to be acquired. Then, these electrodes are connected to the AFE (A) to perform the analog-to-digital conversion. The converted signals are further processed by the microprocessor (B) and transmitted to the software platform (C). Meanwhile, the microprocessor also controls the process of acquisition and ensures stability and precision. After receiving the data transmitted from the microprocessor, the software platform visualizes and stores the signals in real time. Additionally, the software platform can generate visual and auditory stimuli to participants and record the corresponding time. After time synchronization between signals and experiments, the data with specific events can be stored for subsequent analysis.
  • Figure 2: (A) The scheme of 24-channel EEG acquisition in the daisy-chain mode. (B), (C) The front and back of the motherboard. (D), (E) The front and back of the ADC-board. (F) The picture of four ADC-boards, a motherboard and a MP plugged together.
  • Figure 3: The flowchart of the Microprocessor embedded software.
  • Figure 4: (A) The control window. (B) The waveform window. A test signal of single-frequency square wave is displayed, which indicates that the system is working properly. (C) The bottom architecture of the software platform.
  • Figure 5: The results of CMRR in different frequencies.
  • ...and 4 more figures