User customization of the feature generator of an asynchronous brain interface.
A study that customizes the feature generator parameters of an asynchronous Brain Interface (BI) is discussed. The goal is to detect the presence of a certain pattern, in the ongoing EEG, associated with a specific movement and to improve the system's performance. Results of this study show that customization mostly benefits able-bodied subjects with performance improvements of up to 6.8%. We evaluate the performance of our BI using stratified cross-validation scheme. This scheme repeats the analysis on the different cross-validation sets. It is shown that the performances of the system across the different sets are very similar. Thus, we conclude that a robust performance measure of the system can be obtained by using only one of these performance results.
Bashashati
Ali
A
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4. alibs@ece.ubc.ca
Fatourechi
Mehrdad
M
Ward
Rabab K
RK
Birch
Gary E
GE
D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D016287:Computer Peripherals; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D014584:User-Computer Interface