Real-time brain-computer interfacing: A preliminary study using Bayesian learning


DOI: 10.1007/BF02344689

Cite this article as:
Roberts, S.J. & Penny, W.D. Med. Biol. Eng. Comput. (2000) 38: 56. doi:10.1007/BF02344689


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.


Brain-computer interfacing Real-time EEG analysis Biosignal analysis Bayesian learning 

Copyright information

© IFMBE 2000

Authors and Affiliations

  1. 1.Robotics Research Group, Department of Engineering ScienceUniversity of OxfordUK

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