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The non-stationary nature of electroencephalogram (EEG) poses a major challenge for the operation of a brain-computer interface (BCI). This paper proposes a novel Dynamically Weighted Ensemble Classification (DWEC) framework to address the non-stationarity. An ensemble of multiple classifiers are trained on clustered features. The decisions from these multiple classifiers are dynamically combined based on the distances of the cluster centres to each test data sample being classified. Thedoi:10.1088/1741-2560/10/3/036007 pmid:23574821 fatcat:mrygve3tcjdf3n5qf3i436zvea