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2020 | OriginalPaper | Buchkapitel

kNN and SVM Classification for EEG: A Review

verfasst von : M. N. A. H. Sha’abani, N. Fuad, Norezmi Jamal, M. F. Ismail

Erschienen in: InECCE2019

Verlag: Springer Singapore

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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.

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Titel: kNN and SVM Classification for EEG: A Review
verfasst von: M. N. A. H. Sha’abani
N. Fuad
Norezmi Jamal
M. F. Ismail
Verlag: Springer Singapore
Buch: InECCE2019
Print ISBN: 978-981-15-2316-8

Electronic ISBN: 978-981-15-2317-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-981-15-2317-5_47