research-article

Independent components for EEG signal classification

Authors:

Tobie Erhard Olivier,

Barend Jacobus van Wyk,

Yskandar HamamAuthors Info & Claims

ICIIP '16: Proceedings of the 1st International Conference on Intelligent Information Processing

Article No.: 33, Pages 1 - 6

https://doi.org/10.1145/3028842.3028875

Published: 23 December 2016 Publication History

Abstract

This paper addresses movement imagery detection via electroencephalogram (EEG) signal classification. Independent component analysis (ICA) is employed to factorise the time domain EEG signal. A three-layer Neural Network (NN) frame work is constructed to classify the movement imageries using the power spectrum features of independent components (ICs). The main contributions of the paper are reflected in the following points: (1) unlike existing methods, the ICA is not used to reject artifacts but considered as the source for extracting features to train the Neural Network classifiers; (2) a voting NN classification framework is proposed. The experiment results is based on the data obtained from the 2008 Berlin BCI Competition database and shows the proposed method has a high classification capacity.

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Cited By

Gebre-Amlak HNguyen HLowe JNabulsi AChuy N(2018)Spatial Correlation Preserving EEG Dimensionality Reduction Using Machine Learning2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2018.8621106(2583-2589)Online publication date: Dec-2018
https://doi.org/10.1109/BIBM.2018.8621106

Index Terms

Independent components for EEG signal classification
1. Computing methodologies
  1. Machine learning

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ICIIP '16: Proceedings of the 1st International Conference on Intelligent Information Processing

December 2016

358 pages

ISBN:9781450347990

DOI:10.1145/3028842

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Jilin Institute of Chemical Technology: Jilin Institute of Chemical Technology, Jilin, China
Wanfang Data: Wanfang Data, Beijing, China
CNKI: CNKI, Beijing, China
Airiti: Airiti, Taiwan
Guilin: Guilin University of Technology, Guilin, China
Wuhan University of Technology: Wuhan University of Technology, Wuhan, China
Ain Shams University: Ain Shams University, Egypt
International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 December 2016

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Tshwane University of Technology
National Research Fund of South Africa

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ICIIP 2016

Sponsor:

Jilin Institute of Chemical Technology
Wanfang Data
CNKI
Airiti
Guilin
Wuhan University of Technology
Ain Shams University
International Engineering and Technology Institute, Hong Kong

ICIIP 2016: 2016 International Conference on Intelligent Information Processing

December 23 - 25, 2016

Wuhan, China

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ICIIP '16 Paper Acceptance Rate 55 of 165 submissions, 33%;

Overall Acceptance Rate 87 of 367 submissions, 24%

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Cited By

Gebre-Amlak HNguyen HLowe JNabulsi AChuy N(2018)Spatial Correlation Preserving EEG Dimensionality Reduction Using Machine Learning2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2018.8621106(2583-2589)Online publication date: Dec-2018
https://doi.org/10.1109/BIBM.2018.8621106

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