A novel criterion of wavelet packet best basis selection for signal classification with application to brain-computer interfaces.
This study proposes a method to select a wavelet basis for classification. It uses a strategy defined by Wickerhauser and Coifman and proposes a new additive criterion describing the contrast between classes. Its performance is compared with other approaches on simulated signals and on experimental EEG signals for brain-computer interface applications.
Vautrin
Denis
D
Institut de Recherche en Communication et Cybernétique de Nantes, Centrale Nantes, Nantes 44321, France.
Artusi
Xavier
X
Lucas
Marie-Françoise
MF
Farina
Dario
D
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface