dentification and Classification of Electroencephalogram Signals Based on Independent Component Analysis

Volume 16 No 5 (2018)

Chao Zhang , Jing Xu, Su Pan, Yudan Yang

DOI: 10.14704/nq.2018.16.5.1392

Abstract

This paper aims to develop a desirable EEG-based classification algorithm. For this purpose, the discrete wavelet transform was applied to denoise the EEG signals. Then, the brain’s left and right hand movement features were extracted from the denoised signals by the independent component analysis (ICA). Finally, the support vector machine (SVM) classifier was adopted to recognize and classify the movement of the left and right hand actions. The experimental results show that our method achieves the recognition accuracy of 89.5% and 90.6% respectively. The research findings provide a valuable reference for the future research into the BCI system.

Keywords

Electroencephalogram (EEG), Brain Computer Interface (BCI), Independent Component Analysis (ICA), Support Vector Machine (SVM)

Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.