SSVEP-based brain-computer interface for computer control application using SVM classifier

  • Authors

    • Raymond Sutjiadi Informatics Department, Faculty of Information Technology, Institut Informatika Indonesia Surabaya
    • Timothy John Pattiasina Information System Department, Faculty of Information Technology, Institut Informatika Indonesia Surabaya
    • Resmana Lim Electrical Engineering Department, Faculty of Industrial Technology, Petra Christian University Surabaya
    2018-09-26
    https://doi.org/10.14419/ijet.v7i4.16139
  • Brain Computer Interface, Brain Waves, Electroencephalography, Steady State Visually Evoked Potential, Support Vector Machine.
  • In this research, a Brain Computer Interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) for computer control applications using Support Vector Machine (SVM) is presented. For many years, people have speculated that electroencephalographic activities or other electrophysiological measures of brain function might provide a new non-muscular channel that can be used for sending messages or commands to the external world. BCI is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. BCI systems provide a new communication channel for disabled people. Among many different types of the BCI systems, the SSVEP based has attracted more attention due to its ease of use and signal processing. SSVEPs are usually detected from the occipital lobe of the brain when the subject is looking at a twinkling light source. In this paper, SVM is used to classify SSVEP based on electroencephalogram data with proper features. Based on the experiment utilizing a 14-channel Electroencephalography (EEG) device, 80 percent of accuracy can be reached by our SSVEP-based BCI system using Linear SVM Kernel as classification engine.

     

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  • How to Cite

    Sutjiadi, R., John Pattiasina, T., & Lim, R. (2018). SSVEP-based brain-computer interface for computer control application using SVM classifier. International Journal of Engineering & Technology, 7(4), 2722-2728. https://doi.org/10.14419/ijet.v7i4.16139