AI Article Synopsis
- * The process involves transforming electroencephalogram (EEG) signals into distinct frequency components, identifying the best-fit basis for each subject, and using the subband energies as effective features.
- * Experimental results demonstrate that this subject-specific adaptation method leads to better classification performance in identifying motor imagery tasks compared to traditional non-adaptive methods.
Article 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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| http://dx.doi.org/10.1016/j.medengphy.2006.01.009 | DOI Listing |
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