Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition.
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.
Yang
Bang-hua
BH
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. ybh@sjtu.edu.cn
Yan
Guo-zheng
GZ
Yan
Rong-guo
RG
Wu
Ting
T
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D004562:Electrocardiography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface