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稳态视觉诱发电位脑机接口的现场可编程逻辑门阵列实现

  • Yanjun Zhang
  • , Jun Xie
  • , Tao Xue
  • , Guozhi Cao
  • , Guanghua Xu
  • , Min Li
  • Xi'an Jiaotong University

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

To solve the problem that the steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) system requires high computer performance, a SSVEP BCI system based on field programmable gate array (FPGA) and commercial electroencephalogram (EEG) acquisition device is designed by an independent display module based on FPGA. This system realizes the control of video graphics array (VGA) interface. The paradigm patterns corresponding to different flicker frequencies are allocated according to the displaying refresh frames, and the stable display of the paradigm required to induce SSVEP signals can be achieved. Collecting and analyzing the flicker outputs of the designed VGA visual stimulator, the presenting frequencies from the visual stimulator are approximately equal to the required frequencies and can be used for SSVEP BCI experiments. Combined with the designed visual stimulator, the FPGA based EEG signal processing and feature recognition are also implemented in this approach. EEG signals are transmitted to the FPGA end via the serial port, and fast Fourier transform (FFT) is used to analyze the frequency components, and these frequency components are then compared with the presenting frequencies from the visual stimulator. The overall system is verified by experiments. It is revealed that the SSVEP BCI system based on FPGA achieves an average recognition accuracy of 85.25% in the case of four stimulus targets and two seconds of single-trial time window. The proposed system can induce and recognize SSVEP signals effectively and achieve satisfactory recognition results.

投稿的翻译标题Implementation of Steady-State Visual Evoked Potential BCI System Based on Field Programmable Gate Array
源语言繁体中文
页(从-至)158-165
页数8
期刊Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University
54
2
DOI
出版状态已出版 - 10 2月 2020

关键词

  • Brain-computer interface
  • Data processing
  • Feature recognition
  • Field programmable gate array
  • Steady-state visual evoked potential

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