Self-paced BCI using multiple SWT-based classifiers.
The presence of false activations inhibits the use of existing self-paced BCIs in real life applications. We present a new design method for a self-paced BCI that yielded 0% false activations using the data of two subjects. This system obtains templates/shapes of the movement related finger flexion patterns. To obtain the templates, the intentional control data are decomposed into 5 levels using the stationary wavelet transform. Then, ensemble averaging is done. These templates are used to train 5 radial basis function neural networks. This is followed by a majority voting classifier.
Faradji
Farhad
F
Electrical and Computer Engineering, Department, University of British Columbia, Vancouver, V6T 1Z4, Canada. farhadf@ece.ubc.ca
Ward
Rabab K
RK
Birch
Gary E
GE
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual; D006801:Humans; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface