Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain wa...

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Main Authors:	Khorshidtalab, Aida, Salami, Momoh Jimoh Emiyoka, Hamedi , Mahyar
Format:	Conference or Workshop Item
Language:	English
Published:	2012
Subjects:	TA168 Systems engineering
Online Access:	http://irep.iium.edu.my/26891/ http://irep.iium.edu.my/26891/ http://irep.iium.edu.my/26891/1/AidaPaper2012B.pdf

id	iium-26891
recordtype	eprints
spelling	iium-268912013-09-12T03:34:20Z http://irep.iium.edu.my/26891/ Evaluating the effectiveness of time-domain features for motor imagery movements using SVM Khorshidtalab, Aida Salami, Momoh Jimoh Emiyoka Hamedi , Mahyar TA168 Systems engineering Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain waves to desired, understandable commands for a machine. Classification of motor imagery tasks for BMIs is the crucial part. Classification accuracy not only depends on how accurate and robust the classifier is; it is also about data. For well separated data, classifiers such as kernel SVM can handle classification and deliver acceptable results. If a feature provides large interclass difference for different classes, immunity to random noise and chaotic behavior of EEG signal is rationally conformed, which means the applied feature is suitable for classifying EEG signals. In this work, in order to have less computational complexity, time-domain algorithms are employed to motor imagery signals. Extracted features are: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length. Support Vector Machine with polynomial kernel is applied for classification of four different classes of data. The obtained results show that these features have acceptable, distinct values for different these four motor imagery tasks. Maximum classification accuracy belongs to contribution of Willison amplitude as feature and SVM as classifier, with 95.1 percentages accuracy. Where, the lowest is the contribution of Waveform Length and SVM with 31.67 percentages classification accuracy. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/26891/1/AidaPaper2012B.pdf Khorshidtalab, Aida and Salami, Momoh Jimoh Emiyoka and Hamedi , Mahyar (2012) Evaluating the effectiveness of time-domain features for motor imagery movements using SVM. In: International Conference on Computer and Communication Engineering (ICCCE 2012), 3-5 July 2012, Seri Pacific Hotel Kuala Lumpur. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06271348
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TA168 Systems engineering
spellingShingle	TA168 Systems engineering Khorshidtalab, Aida Salami, Momoh Jimoh Emiyoka Hamedi , Mahyar Evaluating the effectiveness of time-domain features for motor imagery movements using SVM
description	Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain waves to desired, understandable commands for a machine. Classification of motor imagery tasks for BMIs is the crucial part. Classification accuracy not only depends on how accurate and robust the classifier is; it is also about data. For well separated data, classifiers such as kernel SVM can handle classification and deliver acceptable results. If a feature provides large interclass difference for different classes, immunity to random noise and chaotic behavior of EEG signal is rationally conformed, which means the applied feature is suitable for classifying EEG signals. In this work, in order to have less computational complexity, time-domain algorithms are employed to motor imagery signals. Extracted features are: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length. Support Vector Machine with polynomial kernel is applied for classification of four different classes of data. The obtained results show that these features have acceptable, distinct values for different these four motor imagery tasks. Maximum classification accuracy belongs to contribution of Willison amplitude as feature and SVM as classifier, with 95.1 percentages accuracy. Where, the lowest is the contribution of Waveform Length and SVM with 31.67 percentages classification accuracy.
format	Conference or Workshop Item
author	Khorshidtalab, Aida Salami, Momoh Jimoh Emiyoka Hamedi , Mahyar
author_facet	Khorshidtalab, Aida Salami, Momoh Jimoh Emiyoka Hamedi , Mahyar
author_sort	Khorshidtalab, Aida
title	Evaluating the effectiveness of time-domain features for motor imagery movements using SVM
title_short	Evaluating the effectiveness of time-domain features for motor imagery movements using SVM
title_full	Evaluating the effectiveness of time-domain features for motor imagery movements using SVM
title_fullStr	Evaluating the effectiveness of time-domain features for motor imagery movements using SVM
title_full_unstemmed	Evaluating the effectiveness of time-domain features for motor imagery movements using SVM
title_sort	evaluating the effectiveness of time-domain features for motor imagery movements using svm
publishDate	2012
url	http://irep.iium.edu.my/26891/ http://irep.iium.edu.my/26891/ http://irep.iium.edu.my/26891/1/AidaPaper2012B.pdf
first_indexed	2023-09-18T20:40:01Z
last_indexed	2023-09-18T20:40:01Z
_version_	1777409302127968256

Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

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