IIUM Repository (IREP)

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

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.

[img] PDF (Evaluating the effectiveness of time-domain features for motor imagery movements using SVM)
Restricted to Registered users only

Download (185kB) | Request a copy

Abstract

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.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 2470/26891
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Prof Momoh-Jimoh Salami
Date Deposited: 12 Sep 2013 11:34
Last Modified: 12 Sep 2013 11:34
URI: http://irep.iium.edu.my/id/eprint/26891

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year