Electroencephalogram (EEG) recorded during motor imagery tasks can be used to move a cursor to a target on a
computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel for the
subjects with neuromuscular disorders. To achieve higher speed and more accuracy to enhance the practical applications
of BCI in computer aid medical systems, the ensemble classifier is used for the single classification. The ERDs at the
electrodes C3 and C4 are calculated and then stacked together into the feature vector for the ensemble classifier. The
ensemble classifier is based on Linear Discriminant Analysis (LDA) and Nearest Neighbor (NN). Furthermore, it
considers the feedback. This method is successfully used in the 2003 international data analysis competition on BCI-tasks
(data set III). The results show that the ensemble classifier succeed with a recognition as 90%, on average, which is
5% and 3% higher than that of using the LDA and NN separately. Moreover, the ensemble classifier outperforms LDA
and NN in the whole time course. With adequate recognition, ease of use and clearly understood, the ensemble classifier
can meet the need of time-requires for single classification.
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