Real-time brain-computer interfacing: a preliminary study using Bayesian learning.
Preliminary results from real-time 'brain-computer interface' experiments are presented. The analysis is based on autoregressive modelling of a single EEG channel coupled with classification and temporal smoothing under a Bayesian paradigm. It is shown that uncertainty in decisions is taken into account under such a formalism and that this may be used to reject uncertain samples, thus dramatically improving system performance. Using the strictest rejection method, a classification performance of 86.5 +/- 6.9% is achieved over a set of seven subjects in two-way cursor movement experiments.
Roberts
S J
SJ
Department of Engineering Science, University of Oxford, UK. sjrob@robots.ox.ac.uk
Penny
W D
WD
D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface