Subject:
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160 000 Neuronal Oscillations Biophysics Cognitive artificial intelligence DCN 3: Neuroinformatics DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication Data Science |
Organization:
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Donders Centre for Cognitive Neuroimaging Biophysics Cognitive Neuroscience SW OZ DCC KI Data Science PI Group Neuronal Oscillations |
Former Organization:
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F.C. Donders Centre for Cognitive Neuroimaging
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Abstract:
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We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods.
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