kNN and SVM classification for EEG: a review
This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model...
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| Format: | Book Section |
| Language: | en |
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Springer Nature
2020
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| Online Access: | http://eprints.uthm.edu.my/2872/1/kNN%20and%20SVM%20classification%20for%20eeg.pdf http://eprints.uthm.edu.my/2872/ |
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| _version_ | 1833417187769974784 |
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| author | Fuad, N. Sha'abani, M.N.A.H. Jamal, Norezmi Ismail, M.F. |
| author2 | Nasir, Ahmad Nor Kasruddin |
| author_facet | Nasir, Ahmad Nor Kasruddin Fuad, N. Sha'abani, M.N.A.H. Jamal, Norezmi Ismail, M.F. |
| author_sort | Fuad, N. |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances. |
| format | Book Section |
| id | my.uthm.eprints-2872 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2020 |
| publisher | Springer Nature |
| record_format | eprints |
| spelling | my.uthm.eprints-28722022-01-02T06:55:01Z http://eprints.uthm.edu.my/2872/ kNN and SVM classification for EEG: a review Fuad, N. Sha'abani, M.N.A.H. Jamal, Norezmi Ismail, M.F. TK7800-8360 Electronics This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances. Springer Nature Nasir, Ahmad Nor Kasruddin Ahmad, Mohd Ashraf Najib, Muhammad Sharfi Abdul Wahab, Yasmin Othman, Nur Aqilah Abd Ghani, Nor Maniha Irawan, Addie Khatun, Sabira Raja Ismail, Raja Mohd Taufika Saari, Mohd Mawardi Daud, Mohd Razali Mohd Faudzi, Ahmad Afif 2020 Book Section PeerReviewed text en http://eprints.uthm.edu.my/2872/1/kNN%20and%20SVM%20classification%20for%20eeg.pdf Fuad, N. and Sha'abani, M.N.A.H. and Jamal, Norezmi and Ismail, M.F. (2020) kNN and SVM classification for EEG: a review. In: Lecture Notes in Electrical Engineering. Springer Nature, pp. 555-565. ISBN 978-981-15-2316-8 |
| spellingShingle | TK7800-8360 Electronics Fuad, N. Sha'abani, M.N.A.H. Jamal, Norezmi Ismail, M.F. kNN and SVM classification for EEG: a review |
| title | kNN and SVM classification for EEG: a review |
| title_full | kNN and SVM classification for EEG: a review |
| title_fullStr | kNN and SVM classification for EEG: a review |
| title_full_unstemmed | kNN and SVM classification for EEG: a review |
| title_short | kNN and SVM classification for EEG: a review |
| title_sort | knn and svm classification for eeg: a review |
| topic | TK7800-8360 Electronics |
| url | http://eprints.uthm.edu.my/2872/1/kNN%20and%20SVM%20classification%20for%20eeg.pdf http://eprints.uthm.edu.my/2872/ |
| url_provider | http://eprints.uthm.edu.my/ |