Effects of the combination of block-shape and face flashing stimuli on a P300 brain computer interface

Xinru Zhang, Jing Jin, Ren Xu, Yangyang Miao, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Brain-computer interface (BCI) provides a new way for the brain to interact with the outside world. One of the common BCI systems is the visual P300 speller. Previous studies have shown that faces can induce strong event-related potentials (ERPs) to achieve high classification accuracy and the block-shaped condition can obtain better performance by increasing the size of the highlighted surface. However, the effects of both blended factors have not yet been studied. Therefore, we proposed a red block-shaped face paradigm (RBFP) combining both block-shape and face. We compared the proposed paradigm with the traditional red face paradigm (RFP) to validate its feasibility and effectiveness. Results indicated that RBFP had better classification accuracy (95.27% vs. 91.11%) and raw bit rate (RBR) (25.84 bit/min vs. 23.43 bit/min) than RFP, and could elicit particularly increased ERPs. Moreover, RBFP obtained a high degree of preference in the subjective assessment. In conclusion, the proposed paradigm combined the advantages of both types of stimuli to obtain a significant improvement in BCI performance, which was meaningful for future BCI studies.

Original languageEnglish
Title of host publicationProceedings - 2020 Chinese Automation Congress, CAC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1898-1904
Number of pages7
ISBN (Electronic)9781728176871
DOIs
Publication statusPublished - 6 Nov 2020
Externally publishedYes
Event2020 Chinese Automation Congress, CAC 2020 - Shanghai, China
Duration: 6 Nov 20208 Nov 2020

Publication series

NameProceedings - 2020 Chinese Automation Congress, CAC 2020

Conference

Conference2020 Chinese Automation Congress, CAC 2020
Country/TerritoryChina
CityShanghai
Period6/11/208/11/20

Keywords

  • brain-computer interface
  • classification accuracy
  • combined paradigm
  • EEG
  • P300

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