288 pp. per issue
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Neural Computation

November 2012, Vol. 24, No. 11, Pages 2900-2923
(doi: 10.1162/NECO_a_00348)
© 2012 Massachusetts Institute of Technology
Adaptive Classification on Brain-Computer Interfaces Using Reinforcement Signals
Article PDF (800.37 KB)

We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows that some existing adaptive classifiers are particular cases of such an EM algorithm. Further, we present a new algorithm for adaptive classification, which we call constrained means adaptive classifier, and show using EEG data and simulated RS that this classifier is able to significantly outperform state-of-the-art adaptive classifiers.