Research Repository

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

Zhou, Sijie and Jin, Jing and Daly, Ian and Wang, Xingyu and Cichocki, Andrzej (2016) 'Optimizing the Face Paradigm of BCI System by Modified Mismatch Negative Paradigm.' Frontiers in Neuroscience, 10. ISSN 1662-4548

[img]
Preview
Text
fnins-10-00444.pdf - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview

Abstract

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 (p < 0.05) and N400s (p < 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 (p < 0.05).

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 29 Jun 2018 16:18
Last Modified: 29 Jun 2018 16:18
URI: http://repository.essex.ac.uk/id/eprint/22341

Actions (login required)

View Item View Item