Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy sets in pattern recognition and machine intelligence
Fuzzy Sets and Systems
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
A local neural classifier for the recognition of EEG patterns associated to mental tasks
IEEE Transactions on Neural Networks
Research on feature extraction algorithms in BCI
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Convolution wavelet packet transform and its applications to signal processing
Digital Signal Processing
Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates
Journal of Medical Systems
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Computer Methods and Programs in Biomedicine
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In this paper, we discuss a subject-based feature extraction method using the fuzzy wavelet packet in brain-computer interfaces (BCIs). The method includes the following three steps: (1) original electroencephalogram (EEG) signals are decomposed with the wavelet packet transform (WPT), which forms many wavelet packet bases; (2) for each subject and each EEG channel, the best basis algorithm based on a fuzzy set criterion is used to find the best-adapted basis for that particular subject and channel; and (3) subband energies included in the best basis form effective features, which are used to discriminate three types of motor imagery tasks. The proposed method is compared with the previous wavelet packet method and the results show that it outperforms the previous one.