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

Simple item page
dc.contributor.authorKhorshidtalab, A.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.contributor.authorHamedi, M.
dc.date.accessioned2019-08-14T10:50:35Z
dc.date.available2019-08-14T10:50:35Z
dc.date.issued2013-08-24
dc.description.abstractThe 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.en_US
dc.identifier.citationKhorshidtalab, A., Salami, M. J. E., & Hamedi, M. (2013). Robust classification of motor imagery EEG signals using statistical time–domain features. Physiological measurement, 34(11), 1563.en_US
dc.identifier.uri10.1088/0967-3334/34/11/1563
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/458
dc.language.isoenen_US
dc.publisherIOP Publishingen_US
dc.subjectElectroencephalogramen_US
dc.subjectBrain–machine interfaceen_US
dc.subjectFeature extractionen_US
dc.subjectSupport vector machineen_US
dc.subjectFuzzy C-meansen_US
dc.subjectMutual informationen_US
dc.titleRobust classification of motor imagery EEG signals using statistical time–domain featuresen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Robust classification of motor imagery EEG signals using statistical time–domain features.pdf
Size:
389.81 KB
Format:
Adobe Portable Document Format
Description:
Article full-text
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.61 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Articles in Electrical and Electronics Engineering