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

作者：Yang Bang hua^*; Yan Guo zheng; Yan Rong guo; Ting, Wu

来源：Medical Engineering & Physics, 2007, 29(1): 48-53.

DOI：10.1016/j.medengphy.2006.01.009

• brain-computer interface (BCI)
• wavelet packet best basis decomposition (WPBBD)
• subject-based feature extraction
• adaptive feature extraction
• electroencephalogram (EEG)

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.

• 出版日期2007-1
• 单位上海交通大学