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An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection
We present a novel and efficient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain–computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coefficients...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660708/ https://www.ncbi.nlm.nih.gov/pubmed/23717804 |
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author | Perseh, Bahram Sharafat, Ahmad R. |
author_facet | Perseh, Bahram Sharafat, Ahmad R. |
author_sort | Perseh, Bahram |
collection | PubMed |
description | We present a novel and efficient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain–computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coefficients of discrete Daubechies 4 wavelet, and for selecting the effective electroencephalogram channels, we utilize an improved binary particle swarm optimization algorithm together with the Bhattacharyya criterion. We tested our proposed scheme on dataset IIb of BCI competition 2005 and achieved 97.5% and 74.5% accuracy in 15 and 5 trials, respectively, using a simple classification algorithm based on Bayesian linear discriminant analysis. We also tested our proposed scheme on Hoffmann's dataset for eight subjects, and achieved similar results. |
format | Online Article Text |
id | pubmed-3660708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-36607082013-05-28 An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection Perseh, Bahram Sharafat, Ahmad R. J Med Signals Sens Original Article We present a novel and efficient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain–computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coefficients of discrete Daubechies 4 wavelet, and for selecting the effective electroencephalogram channels, we utilize an improved binary particle swarm optimization algorithm together with the Bhattacharyya criterion. We tested our proposed scheme on dataset IIb of BCI competition 2005 and achieved 97.5% and 74.5% accuracy in 15 and 5 trials, respectively, using a simple classification algorithm based on Bayesian linear discriminant analysis. We also tested our proposed scheme on Hoffmann's dataset for eight subjects, and achieved similar results. Medknow Publications & Media Pvt Ltd 2012 /pmc/articles/PMC3660708/ /pubmed/23717804 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Perseh, Bahram Sharafat, Ahmad R. An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection |
title | An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection |
title_full | An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection |
title_fullStr | An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection |
title_full_unstemmed | An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection |
title_short | An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection |
title_sort | efficient p300-based bci using wavelet features and ibpso-based channel selection |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660708/ https://www.ncbi.nlm.nih.gov/pubmed/23717804 |
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