Subject-based feature extraction using fuzzy wavelet packet in brain–computer interfaces

Upper limb complex movements decoding from pre-movement EEG signals using wavelet common spatial patterns

“…Daubechies mother wavelets have resulted in more accurate BCI systems in other studies [31,32]. Hence, in this study, we used the db4 mother wavelet for SWT decomposition.…”

“…According to the obtained results, high-frequency sub-bands had better performance compared to low frequency. The results showed that using either the beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) or the gamma (32-64 Hz) sub-bands, a classification accuracy over 90% can be obtained.…”

Intelligent Artificial Ants based feature extraction from wavelet packet coefficients for biomedical signal classification

“…As a final step, we compare the results obtained with the approach presented by Yang et al [28], in which they utilized the FWP method to design a subject dependant feature extraction for a BCI system. A maximum accuracy of 76% was achieved using Yang method, while in our experiment the FWP achieved a maximum of 81.73%, and the IAA achieved a maximum of 83.4%.…”

Computational Intelligence Approaches to Brain Signal Pattern Recognition

“…It is hard to assume that there is a crisp unequivocal association between characteristic patterns of brain's electrophysiological activity and classes of particular mental tasks. As suggested in (Yang et al, 2007), a mixture of some residual correlates of different cognitive processes should always be expected. This facet of uncertainty related to brain state class assignments is perceived as an inherent feature of brain signal pattern recognition.…”