Abstract
Basically brain-computer interfaces (BCIs) make use of multichannel electroencephalogram (EEG) devices. Motor imagery tasks are at the core of BCIs and their performance in real-time systems need analysis of huge amounts of signal data coming from multiple channels. The process can be enhanced by selecting a subset of EEG channels to get away with noisy and irrelevant channels. Usage of lesser channels makes the system more convenient to be used in real time applications. This paper deals with the problem of optimal EEG channel selection using enhanced version of differential evolutionary (DE) algorithm. It is straightforward to use the task-specific channels pertaining to the stimulus like \(C_3\), \(C_Z\) and \(C_4\) in case of motor imagery. However, our findings show that the classification performance improves by selecting the optimal channels over the task-specific channels. Also, the performance of the optimal channels is compared with the performance achieved by using all the channels and it is seen that the proposed enhanced variant of DE is beneficial in this regard.
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