Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
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- 作者：Jasmin Kevrica ; Abdulhamit Subasib ; absubasi@effatuniversity.edu.sa" class="auth_mail" title="E-mail the corresponding author
- 关键词：Empirical mode decomposition (EMD) ; Discrete wavelet transform (DWT) ; Wavelet packet decomposition (WPD) ; Motor imagery (MI) ; Brain computer interface (BCI) ; Higher order statistics (HOS) ; BCI competition III dataset IVa
- 刊名：Biomedical Signal Processing and Control
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：31
- 期：Complete
- 页码：398-406
- 全文大小：714 K
文摘
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Wavelet Packet Decomposition was superior to other signal decomposition methods.
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The inclusion of higher frequency ranges improves the classification in BCI systems.
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Shortcomings of wavelets have been compensated by higher order statistics features.
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The proposed model may be used to enhance the current rehabilitation therapies.