Abstract
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.
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Roberts, S.J., Penny, W.D. Real-time brain-computer interfacing: A preliminary study using Bayesian learning. Med. Biol. Eng. Comput. 38, 56–61 (2000). https://doi.org/10.1007/BF02344689
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DOI: https://doi.org/10.1007/BF02344689