A Comparison of Facial P300-Speller Paradigm Based on Famous Face and the Familiar Face

Zhaohua Lu, Ning Gao, Weiwei Zhou, Jingjing Yang, Jinglong Wu, Qi Li

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

Abstract

Some studies about face recognition showed the human face with more relevant to oneself induces larger amplitude ERPs than the famous face. The famous face stimulus has been used to the P300-speller paradigm and improved the performance of P300-speller system. In our study, we designed a novel P300-speller paradigm using familiar face as stimulus, and the famous face P300-speller paradigm was as the control paradigm. We compared the ERPs and the classification accuracy between the two paradigms. The results showed that larger ERPs amplitude in the proposed paradigm than that in the control paradigm, and the average classification of proposed paradigm was greater than that in the control paradigm, while we didn't find the significant difference for classification accuracy between the two paradigms.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
EditorsQingli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728148526
DOIs
Publication statusPublished - Oct 2019
Event12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 - Huaqiao, China
Duration: Oct 19 2019Oct 21 2019

Publication series

NameProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

Conference

Conference12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
CountryChina
CityHuaqiao
Period10/19/1910/21/19

Keywords

  • Brain-computer interface
  • P300 speller
  • familiar face
  • famous face
  • paradigm

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Information Systems and Management

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