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

S J Roberts, W D Penny

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)56-61
Number of pages6
JournalMedical and Biological Engineering and Computing
Issue number1
Publication statusPublished - Jan 2000


  • Bayes Theorem
  • Brain
  • Electroencephalography
  • Humans
  • Computer-Assisted Signal Processing
  • User-Computer Interface

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