Periodicity detection for BCI based on periodic code modulation visual evoked potentials

Masaki Nakanishi, Yasue Mitsukura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we studied the brain computer interface (BCI) based on periodic code modulation visual evoked potential (VEP). The code modulation VEP (c-VEP) is one of electroencephalogram (EEG)-based BCI methods, and can achieve high speed communication. In this method, by identifying a pseudorandom binary code (PRBC) that modulates visual stimulus from measured EEG, we can transfer the command related with the PRBC into external devices. However, the communication speed becomes slow inversely with increased number of commands. In order to solve this problem, we proposed extended c-VEP method using periodic pseudorandom binary codes. In this method, we identify the periodicity from the EEG by using autocorrelation, and the command related with periodicity of the EEG is transferred. As a result of computer simulation, we were able to detect the periodicity of the EEG. Therefore, we verified the feasibility of the periodic pseudorandom binary codes for VEP-based BCI.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages665-668
Number of pages4
DOIs
Publication statusPublished - 2012 Oct 23
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 2012 Mar 252012 Mar 30

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period12/3/2512/3/30

Keywords

  • Autocorrelation
  • Brain-computer interfaces
  • Electroencephalography
  • Visual evoked potentials

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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