Efficient feature extraction framework for EEG signals classification

Weijie Ren, Min Han, Jun Wang, Dan Wang, Tieshan Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

Feature extraction and classification for EEG signals are key technologies in medical applications. This paper proposes an efficient feature extraction framework that combines hybrid feature extraction and feature selection method. In order to fully exploit information from EEG signals, several feature extraction methods of different types are applied, which are autoregressive model, discrete wavelet transform, wavelet packet transform and sample entropy. After information fusion, feature selection methods are introduced to deal with redundant and irrelevant information, which is advantageous to classification. In this phase, global optimization strategy based on binary particle swarm optimization (BPSO) is presented to enhance the performance of feature selection. To evaluate the results of feature extraction, experiments of class separability are conducted. Classification results on EEG dataset of university of Bonn show the superiority of the proposed method.

Original languageEnglish
Title of host publication7th International Conference on Intelligent Control and Information Processing, ICICIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages167-172
Number of pages6
ISBN (Electronic)9781509021550
DOIs
Publication statusPublished - 23 Mar 2017
Externally publishedYes
Event7th International Conference on Intelligent Control and Information Processing, ICICIP 2016 - Siem Reap, Cambodia
Duration: 1 Dec 20164 Dec 2016

Publication series

Name7th International Conference on Intelligent Control and Information Processing, ICICIP 2016 - Proceedings

Conference

Conference7th International Conference on Intelligent Control and Information Processing, ICICIP 2016
Country/TerritoryCambodia
CitySiem Reap
Period1/12/164/12/16

Keywords

  • EEG signals classification
  • Feature extraction
  • Feature selection
  • Feature transformation

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