Robust classification of motor imagery EEG signals using statistical time–domain features

The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain–machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time– domain...

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Main Authors:	Khorshidtalab, Aida, Salami, Momoh Jimoh Eyiomika, Hamedi , Mahyar
Format:	Article
Language:	English
Published:	IOP Publishing 2013
Subjects:	QP Physiology
Online Access:	http://irep.iium.edu.my/33034/ http://irep.iium.edu.my/33034/ http://irep.iium.edu.my/33034/ http://irep.iium.edu.my/33034/1/0967-3334_34_11_1563.pdf

id	iium-33034
recordtype	eprints
spelling	iium-330342014-01-08T01:45:36Z http://irep.iium.edu.my/33034/ Robust classification of motor imagery EEG signals using statistical time–domain features Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar QP Physiology The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain–machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time– domain features only if the right threshold value is defined for them. To overcome the drawback of going through trial and error for the determination of a suitable threshold value, modified WAMP and modified SSC are proposed in this paper. Besides, a comprehensive assessment of statistical time–domain features in which their effectiveness is evaluated with a support vector machine (SVM) is presented. To ensure the accuracy of the results obtained by the SVM, the performance of each feature is reassessed with supervised fuzzy C-means. The general assessment shows that every subject had at least one of his performances near or greater than 80%. The obtained results prove that for BMI applications, in which a few errors can be tolerated, these combinations of feature–classifier are suitable. Moreover, features that could perform satisfactorily were selected for feature combination. Combinations of the selected features are evaluated with the SVM, and they could significantly improve the results, in some cases, up to full accuracy. IOP Publishing 2013-10-24 Article PeerReviewed application/pdf en http://irep.iium.edu.my/33034/1/0967-3334_34_11_1563.pdf Khorshidtalab, Aida and Salami, Momoh Jimoh Eyiomika and Hamedi , Mahyar (2013) Robust classification of motor imagery EEG signals using statistical time–domain features. Physiological Measurement, 34 . pp. 1563-1579. ISSN 1361-6579 (O), 0967-3334 (P) http://iopscience.iop.org/0967-3334/34/11/1563 10.1088/0967-3334/34/11/1563
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QP Physiology
spellingShingle	QP Physiology Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar Robust classification of motor imagery EEG signals using statistical time–domain features
description	The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain–machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time– domain features only if the right threshold value is defined for them. To overcome the drawback of going through trial and error for the determination of a suitable threshold value, modified WAMP and modified SSC are proposed in this paper. Besides, a comprehensive assessment of statistical time–domain features in which their effectiveness is evaluated with a support vector machine (SVM) is presented. To ensure the accuracy of the results obtained by the SVM, the performance of each feature is reassessed with supervised fuzzy C-means. The general assessment shows that every subject had at least one of his performances near or greater than 80%. The obtained results prove that for BMI applications, in which a few errors can be tolerated, these combinations of feature–classifier are suitable. Moreover, features that could perform satisfactorily were selected for feature combination. Combinations of the selected features are evaluated with the SVM, and they could significantly improve the results, in some cases, up to full accuracy.
format	Article
author	Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar
author_facet	Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar
author_sort	Khorshidtalab, Aida
title	Robust classification of motor imagery EEG signals using statistical time–domain features
title_short	Robust classification of motor imagery EEG signals using statistical time–domain features
title_full	Robust classification of motor imagery EEG signals using statistical time–domain features
title_fullStr	Robust classification of motor imagery EEG signals using statistical time–domain features
title_full_unstemmed	Robust classification of motor imagery EEG signals using statistical time–domain features
title_sort	robust classification of motor imagery eeg signals using statistical time–domain features
publisher	IOP Publishing
publishDate	2013
url	http://irep.iium.edu.my/33034/ http://irep.iium.edu.my/33034/ http://irep.iium.edu.my/33034/ http://irep.iium.edu.my/33034/1/0967-3334_34_11_1563.pdf
first_indexed	2023-09-18T20:47:42Z
last_indexed	2023-09-18T20:47:42Z
_version_	1777409786156941312

Robust classification of motor imagery EEG signals using statistical time–domain features

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