P300-based Brain–Computer Interface with Latency Estimation Using ABC-based Interval Type-2 Fuzzy Logic System

Chung Hsien Kuo*, Yu Cheng Kuo, Hung Chyun Chou, Yi Tseng Lin

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

4 Scopus citations

Abstract

P300 is a brain–computer interface (BCI) modality which reflects brains’ processes in stimulus events. Visual stimuli are usually used to elicit event-related P300 components. However, depending on different subjects’ conditions and their current cerebral loads, P300 components occur at posterior to stimuli from 250 to 600 ms roughly. These subjects’ dependent variations affect the performance of BCI. Thus, an estimation model that estimated an appropriate interval for P300 feature extraction is discussed in this paper. An interval type-2 fuzzy logic system trained by artificial bee-colony algorithm was used to find the latency of elicited P300 with a certain range by means of steady-state visually evoked potential. A support vector machine classifier was adopted to classify extracted epochs into target and non-target stimuli. Seven subjects were involved in experiments. Results showed that the performance of information transfer rate was improved by 1.28 % on average if the proposed latency-estimation approach was introduced.

Original languageEnglish
Pages (from-to)529-541
Number of pages13
JournalInternational Journal of Fuzzy Systems
Volume19
Issue number2
DOIs
StatePublished - 01 04 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016, Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg.

Keywords

  • Artificial bee-colony algorithm
  • Brain–computer interface
  • Interval type-2 fuzzy logic system
  • P300
  • Steady-state visually evoked potential

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