Design of Human-Computer Interaction Control System Based on Steady-state Visual Evoked Encephalography (SSVEP)

Leipo Liu

doi:10.18282/jnt.v3i1.3295

Leipo Liu Henan University of Science

Ariticle ID: 3295

62 Views, 4 PDF Downloads

DOI: https://doi.org/10.18282/jnt.v3i1.3295

Keywords: Steady-State Visual Evoked Eeg, Man-Machine Interaction, Control System

Abstract

With the continuous development of science and technology, the research of EEG has gradually made great achievements. During this time, a technique called mechanized brain interface was also being used. This technology is based on the computer technology, using the EEG signals generated by the neural activities of the human brain to link with the external devices, so as to form a brain as the signal source, through the signal transmission to the external devices to control the external devices of an emerging technology. The visual system collects information from the outside world, and the EEG signals transmit these signals to the brain. Finally, the brain analyzes and makes judgments so as to give instructions to the mechanical parts, and finally the mechanical parts realize the effect. The experiment found that the system can accurately collect the visual information and the generated EEG signals can be timely display conversion, and finally these signals will be converted into digital signals sent to the calculation of the processing system. In this paper, human-computer interaction control as the entry point, through the analysis and design of the EEG signals of the human brain, and finally designed the ability system that can enable people with physical disabilities to obtain autonomous activities.

References

Dong YB, Wang L, Wang LL, et al. A portable steady-state vision-induced EEG signal acquisition system design [J]. International Journal of Biomedical Engineering, 2019, 42 (3): 222-226.

Zhao B. Research on Mechanical Arm System Based on Steady-state Visual evoked Potential Brain-Machine Interface Control [D]. Peking Union Medical College; Chinese Academy of Medical Sciences; Medical Department of Tsinghua University; Chinese Medical Academy of Peking Union Medical College, 2018.

Hu Y. Study on Identification Based on Steady State Vision [D]. Xi'an University of Electronic Technology, 2020.

Zhang YJ, Xie J, Xue T, et al. Field-programmable logic gate array realization of steady-state visual induced potential brain machine interface [J]. Journal of Xi'an Jiaotong University, 2020, 054 (002): 158-165.

Du GJ, Xie J, Zhang YB, et al. Deep learning method for target recognition of steady-state visual evoked potential brain-computer interface [J]. Journal of Xi 'an Jiaotong University, 2019, 053 (011): 42-48.

Chen XG, Xu SP. Frequency Response Characteristics of SVR [J]. Beijing Biomedical Engineering, 2018 (3): 259-264.

Wang L, Kong WZ. A SSVEP Visual Stimulator Design [J]. Journal of Hangzhou University of Electronic Science and Technology (Natural Science Edition), 2019, 039 (004): 24-28.

Liu BS. Design Research of Digital Art Human-Computer Interaction Control System [J]. TV Technology, 2018, v.42; No.507 (10): 144-148.

Sun Y, Wang CF, Guo CS. Brain-based exercise method:, CN106108893B [P]. 2019.

Tu P, Shen R, Huang C. Design of human-computer interaction system based on machine vision. Journal of Capital Normal University (Natural Science Edition), 2019, 40 (004): 24-27.