An efficient feature extraction scheme for classification of mental tasks based on inter-channel correlation in wavelet domain utilizing EEG signal. (August 2020)

• Record Type: Journal Article
• Authors: Rahman, Md. Mostafizur; Fattah, Shaikh Anowarul
• Is Part Of: Biomedical signal processing and control. Volume 61(2020)
• Publication Date: 2020-08
• Subjects: Electroencephalogram (EEG) -- Brain computer interface (BCI) -- Wavelet packet decomposition (WPD) -- Inter-channel correlation -- Support vector machine (SVM); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102033
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Alternative approach of obtaining band limited signals using filter. Effect of using various classical BCI classifiers. Generalization of the proposed method for BCI. Detail explanation of how the number of levels in wavelet decomposition is chosen and the equivalent range of frequencies obtained by each wavelets function. Abstract: In this paper, an efficient scheme of extracting features from EEG signal is proposed for mental task classification based on inter-channel relationship in wavelet domain. It is shown that use of wavelet domain inter-channel relationship can drastically improve the classification performance obtained by conventional wavelet statistics. Both multi-level wavelet decomposition and node reconstruction are utilized for proposed inter-channel correlation feature extraction. It is expected that the correlation obtained from different combination of channels will be different for various mental tasks depending on the nature of the stimulus generated in the brain and thus can provide distinctive features. Support vector machine (SVM) classifier is used to carry out classification of five different mental tasks obtained from an openly accessible EEG dataset. It is found that the proposed scheme can classify mental tasks with a very high level of accuracy compared to that obtained by some existing methods.