Table 3. Evaluation parameters of ensemble classifiers with overlapped partitioning on data set B (BCI competition III data set II).

Evaluationmethod	#training letters	#test letters			#training data for aweak learner (ERPs)
Limited training data(first 5 letters)	5 letters (900 ERPs)	100 letters	55555	12345	180360540720900
Full training data	85 letters	100 letters	1717171717171717171717171717171717	1234567891011121314151617	9001800270036004500540063007200810090009900108001170012600135001440015300

The ensemble classifiers were trained on limited training data (900 training data ) or full training data (15300 training data). The number of weak learners and the number of blocks were parameters used in the overlapped partitioning. These evaluation methods and the parameters determine the amount of training data for a weak learner in an ensemble classifier. The number of training data for a weak learner (#training data for a weak learner) can be computed by given training ERPs × /.