Optimizing the Face Paradigm of BCI System by Modified Mismatch Negative Paradigm

Many recent studies have focused on improving the performance of event-related potential (ERP) based brain computer interfaces (BCIs). The use of a face pattern has been shown to obtain high classification accuracies and information transfer rates (ITRs) by evoking discriminative ERPs (N200 and N400...

Full description

Saved in:
Bibliographic Details
Published inFrontiers in Neuroscience Vol. 10; p. 444
Main Authors Zhou, Sijie, Jin, Jing, Daly, Ian, Wang, Xingyu, Cichocki, Andrzej
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media SA 07.10.2016
Frontiers Research Foundation
Frontiers Media S.A
Subjects
Online AccessGet full text
ISSN1662-453X
1662-4548
1662-453X
DOI10.3389/fnins.2016.00444

Cover

More Information
Summary:Many recent studies have focused on improving the performance of event-related potential (ERP) based brain computer interfaces (BCIs). The use of a face pattern has been shown to obtain high classification accuracies and information transfer rates (ITRs) by evoking discriminative ERPs (N200 and N400) in addition to P300 potentials. Recently, it has been proved that the performance of traditional P300-based BCIs could be improved through a modification of the mismatch pattern. In this paper, a mismatch inverted face pattern (MIF-pattern) was presented to improve the performance of the inverted face pattern (IF-pattern), one of the state of the art patterns used in visual-based BCI systems. Ten subjects attended in this experiment. The result showed that the mismatch inverted face pattern could evoke significantly larger vertex positive potentials ( < 0.05) and N400s ( < 0.05) compared to the inverted face pattern. The classification accuracy (mean accuracy is 99.58%) and ITRs (mean bit rate is 27.88 bit/min) of the mismatch inverted face pattern was significantly higher than that of the inverted face pattern ( < 0.05).
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience
Reviewed by: Dan Zhang, Tsinghua University, China; Roberto C. Sotero, University of Calgary, Canada; Erwei Yin, China Astronaut Research and Training Center, China
Edited by: Srikantan S. Nagarajan, University of California, San Francisco, USA
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2016.00444