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...
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| Published in | Frontiers in Neuroscience Vol. 10; p. 444 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media SA
07.10.2016
Frontiers Research Foundation Frontiers Media S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1662-453X 1662-4548 1662-453X |
| DOI | 10.3389/fnins.2016.00444 |
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| 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). |
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| 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 |