Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition

Medical Engineering & Physics
Bang-hua YangTing Wu

Abstract

In this paper we discuss a subject-based feature extraction method using wavelet packet best basis decomposition (WPBBD) in brain-computer interfaces (BCIs). The idea is to employ the wavelet packet best basis algorithm to adapt to each subject separately. Firstly, original electroencephalogram (EEG) signals are decomposed to a given level by wavelet packet transform. Secondly, for each subject, the best basis algorithm is used to find the best-adapted basis for that particular subject. Finally, subband energies contained in the best basis are used as effective features. Adaptive and specific features of a subject are so obtained. Three different motor imagery tasks of six subjects are discriminated using the above features. Experiment results show that the subject-based adaptation method yields significantly higher classification performance than the non-subject-based adaptation and non-adaptive approaches.

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Citations

Apr 17, 2016·Computer Methods and Programs in Biomedicine·Banghua YangYunyuan Zhang
Mar 1, 2013·IEEE Transactions on Bio-medical Engineering·Neethu RobinsonCuntai T Guan
Aug 15, 2008·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Pawel HermanDamien Coyle
Sep 11, 2013·Journal of Neural Engineering·Neethu RobinsonKeng Peng Tee
Oct 1, 2013·Reviews in the Neurosciences·Alexis Ortiz-Rosario, Hojjat Adeli
Sep 6, 2019·Australasian Physical & Engineering Sciences in Medicine·Kamel Mebarkia, Aicha Reffad
Jul 12, 2013·Journal of Neural Engineering·J Asensio-CuberoR Palaniappan

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