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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/33034/1/0967-3334_34_11_1563.pdf |
| _version_ | 1796878333348151296 |
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| author | Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar |
| author_facet | Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar |
| author_sort | Khorshidtalab, Aida |
| collection | IIUM |
| 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. |
| first_indexed | 2024-03-05T23:20:02Z |
| format | Article |
| id | oai:generic.eprints.org:33034 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T23:20:02Z |
| publishDate | 2013 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | oai:generic.eprints.org: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 |
| spellingShingle | QP Physiology Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Hamedi , Mahyar Robust classification of motor imagery EEG signals using statistical time–domain features |
| title | 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_short | 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 |
| topic | QP Physiology |
| url | http://irep.iium.edu.my/33034/1/0967-3334_34_11_1563.pdf |
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