Classification accuracy improvement of chromatic and high–frequency code–modulated visual evoked potential–based BCI

Daiki Aminaka, Shoji Makino, Tomasz M. Rutkowski*

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

We present results of a classification improvement approach for a code–modulated visual evoked potential (cVEP) based brain– computer interface (BCI) paradigm using four high–frequency flashing stimuli. Previously published research reports presented successful BCI applications of canonical correlation analysis (CCA) to steady–state visual evoked potential (SSVEP) BCIs. Our team already previously proposed the combined CCA and cVEP techniques’ BCI paradigm. The currently reported study presents the further enhanced results using a support vector machine (SVM) method in application to the cVEP–based BCI.

Original languageEnglish
Title of host publicationBrain Informatics and Health - 8th International Conference, BIH 2015, Proceedings
EditorsYike Guo Y., Sean Hill S., Karl Friston, Hanchuan Peng, Aldo Faisal A.
PublisherSpringer Verlag
Pages232-241
Number of pages10
ISBN (Print)9783319233437
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom
Duration: 2015 Aug 302015 Sep 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9250
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Brain Informatics and Health, BIH 2015
Country/TerritoryUnited Kingdom
CityLondon
Period15/8/3015/9/2

Keywords

  • Brain–computer interfaces
  • CVEP
  • EEG classification
  • ERP

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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