The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

Journal: Journal of neuroscience methods
Published Date: Jan 1, 2017

Abstract

BACKGROUND: Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control.

Authors

  • Teng Ma
    Honghui Hospital, Xi'an Jiaotong University, Xi'an, China.
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Hao Yang
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China.
  • Xulin Lv
    Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
  • Peiyang Li
    Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
  • Tiejun Liu
    Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
  • DeZhong Yao
    The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Peng Xu
    Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

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