kNN and SVM classification for EEG: a review

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

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Abstract

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.

Item Type:	Book Section
Uncontrolled Keywords:	Electroencephalogram (EEG); Classification; kNN; SVM
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Electrical and Electronic Engineering > Department of Electronic Enngineering
Depositing User:	Mrs. Siti Noraida Miskan
Date Deposited:	02 Jan 2022 06:55
Last Modified:	02 Jan 2022 06:55
URI:	http://eprints.uthm.edu.my/id/eprint/2872

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