Evaluation of time-domain features for motor imagery movements using FCM and SVM

Brain–Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the c...

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Main Authors:	Khorshidtalab, Aida, Salami, Momoh Jimoh Eyiomika, Hamedi , Mahyar
Format:	Conference or Workshop Item
Language:	English
Published:	2012
Subjects:	TA168 Systems engineering
Online Access:	http://irep.iium.edu.my/26866/ http://irep.iium.edu.my/26866/ http://irep.iium.edu.my/26866/1/AidaPaper2012A.pdf

id	iium-26866
recordtype	eprints
spelling	iium-268662012-11-27T04:02:40Z http://irep.iium.edu.my/26866/ Evaluation of time-domain features for motor imagery movements using FCM and SVM Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar TA168 Systems engineering Brain–Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the core part. Classification accuracy not only depends on how capable the classifier is but also it is about the input data. Feature extraction is to highlight the properties of signal that make it distinct from the signal of the other mental tasks. Performance of BMIs directly depends on the effectiveness of the feature extraction and classification algorithms. If a feature provides large interclass difference for different classes, the applied classifier exhibits a better performance. In order to attain less computational complexity, five timedomain procedure, namely: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length are used for feature extraction of EEG signals. Two classifiers are applied to assess the performance of each feature-subject. SVM with polynomial kernel is one of the applied nonlinear classifier and supervised FCM is the other one. The performance of each feature for input data are evaluated with both classifiers and classification accuracy is the considered common comparison parameter. 2012-08-09 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/26866/1/AidaPaper2012A.pdf Khorshidtalab, Aida and Salami, Momoh Jimoh Eyiomika and Hamedi , Mahyar (2012) Evaluation of time-domain features for motor imagery movements using FCM and SVM. In: 2012 Ninth International Joint Conference on Computer Science and Software Engineering (JCSSE'), 30 May-1 June 2012, Bangkok, Thailand . http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6261918&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A6261804)
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 Eyiomika Hamedi , Mahyar Evaluation of time-domain features for motor imagery movements using FCM and SVM
description	Brain–Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the core part. Classification accuracy not only depends on how capable the classifier is but also it is about the input data. Feature extraction is to highlight the properties of signal that make it distinct from the signal of the other mental tasks. Performance of BMIs directly depends on the effectiveness of the feature extraction and classification algorithms. If a feature provides large interclass difference for different classes, the applied classifier exhibits a better performance. In order to attain less computational complexity, five timedomain procedure, namely: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length are used for feature extraction of EEG signals. Two classifiers are applied to assess the performance of each feature-subject. SVM with polynomial kernel is one of the applied nonlinear classifier and supervised FCM is the other one. The performance of each feature for input data are evaluated with both classifiers and classification accuracy is the considered common comparison parameter.
format	Conference or Workshop Item
author	Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar
author_facet	Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar
author_sort	Khorshidtalab, Aida
title	Evaluation of time-domain features for motor imagery movements using FCM and SVM
title_short	Evaluation of time-domain features for motor imagery movements using FCM and SVM
title_full	Evaluation of time-domain features for motor imagery movements using FCM and SVM
title_fullStr	Evaluation of time-domain features for motor imagery movements using FCM and SVM
title_full_unstemmed	Evaluation of time-domain features for motor imagery movements using FCM and SVM
title_sort	evaluation of time-domain features for motor imagery movements using fcm and svm
publishDate	2012
url	http://irep.iium.edu.my/26866/ http://irep.iium.edu.my/26866/ http://irep.iium.edu.my/26866/1/AidaPaper2012A.pdf
first_indexed	2023-09-18T20:39:58Z
last_indexed	2023-09-18T20:39:58Z
_version_	1777409299026280448

Evaluation of time-domain features for motor imagery movements using FCM and SVM

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