EEG-based motor imagery classification accuracy improves with gradually increased channel number

The question of how many channels should be sed for classification remains a key issue in the study of Brain-Computer Interface. Several studies have shown that a reduced number of channels can achieve the optimal classification accuracy in the offline analysis of motor imagery paradigm, which does not have real-time feedback as in the online control. However, for the cursor movement control paradigm, it remains unclear as to how many channels should be selected in order to achieve the optimal classification. In the present study, we gradually increased the number of channels, and adopted the time-frequency- spatial synthesized method for left and right motor imagery classification. We compared the effect of increasing channel number in two datasets, an imagery-based cursor movement control dataset and a motor imagery tasks dataset. Our results indicated that for the former dataset, the more channels we used, the higher the accuracy rate was achieved, which is in contrast to the finding in the latter dataset that optimal performance was obtained at a subset number of channels. When gradually increasing the number of channels from 2 to all in the analysis of cursor movement control dataset, the average training and testing accuracies from three subjects improved from 68.7% to 90.4% and 63.7% to 87.7%, respectively.