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

• Published in: Medical engineering & physics Vol. 29; no. 1; pp. 48 - 53
• Main Authors: Yang, Bang-hua, Yan, Guo-zheng, Yan, Rong-guo, Wu, Ting
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.01.2007
• Subjects: Adaptive feature extraction; Adult; Algorithms; Artificial Intelligence; Brain - physiology; Brain–computer interface (BCI); Electrocardiography - methods; Electroencephalogram (EEG); Evoked Potentials - physiology; Female; Humans; Imagination - physiology; Male; Pattern Recognition, Automated - methods; Radiology; Reproducibility of Results; Sensitivity and Specificity; Subject-based feature extraction; User-Computer Interface; Wavelet packet best basis decomposition (WPBBD); Adaptive feature extraction; Subject-based feature extraction; Electroencephalogram (EEG); Wavelet packet best basis decomposition (WPBBD); Brain–computer interface (BCI)
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2006.01.009

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.