EEG Filtering Optimization for Code–Modulated Chromatic Visual Evoked Potential–Based Brain–Computer Interface

  • Daiki Aminaka
  • Shoji Makino
  • Tomasz M. Rutkowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9359)

Abstract

We present visual BCI classification accuracy improved results after application of high– and low–pass filters to an electroencephalogram (EEG) containing code–modulated visual evoked potentials (cVEPs). The cVEP responses are applied for the brain–computer interface (BCI) in four commands paradigm mode. The purpose of this project is to enhance BCI accuracy using only the single trial cVEP response. We also aim at identification of the most discriminable EEG bands suitable for the broadband visual stimuli. We report results from a pilot study optimizing the EEG filtering using infinite impulse response filters in application to feature extraction for a linear support vector machine (SVM) classification method. The goal of the presented study is to develop a faster and more reliable BCI to further enhance the symbiotic relationships between humans and computers.

Keywords

Brain–computer interface ERP EEG classification cVEP 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daiki Aminaka
    • 1
  • Shoji Makino
    • 1
  • Tomasz M. Rutkowski
    • 1
  1. 1.Life Science Center of TARA at University of TsukubaTsukubaJapan

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