InECCE2019 pp 555-565 | Cite as

kNN and SVM Classification for EEG: A Review

  • M. N. A. H. Sha’abaniEmail author
  • N. Fuad
  • Norezmi Jamal
  • M. F. Ismail
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)


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.


Electroencephalogram (EEG) Classification kNN SVM 



The authors would like to thank the Research Management Center of Universiti Tun Hussein Onn Malaysia (UTHM) by funding this study under the Tier 1 research grant code H268 and U923. Appreciation also goes to the Faculty of Electrical Engineering, Centre of Diploma Studies, members of Artificial Intelligent Laboratory and Brainwave Research Group (BRG) for their support.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • M. N. A. H. Sha’abani
    • 1
    Email author
  • N. Fuad
    • 2
    • 3
  • Norezmi Jamal
    • 2
  • M. F. Ismail
    • 1
  1. 1.Centre for Diploma StudiesUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  2. 2.Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  3. 3.Computational, Signal, Imaging and Intelligent Focus Group (CSII)Universiti Tun Hussein Onn MalaysiaParit RajaMalaysia

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