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The classification of electroencephalography (EEG) signals is useful in awide range of applications such as seizure detection/prediction, motor imageryclassification, emotion classification and drug effects diagnosis, amongstothers. With the large number of EEG channels acquired, it has become vitalthat efficient data-reduction methods are developed, with varying importancefrom one application to another. It is also important that onlineclassification is achieved during EEG recording for many applications, tomonitor changes as they happen. In this paper we introduce a method based onMutual Information (MI), for channel selection. Obtained results show thatwhilst there is a penalty on classification accuracy scores, promising speed-upgains can be achieved using MI techniques. Using MI with signal epochs (3secs)containing signal transitions enhances these speed-up gains. This work isexploratory and we suggest further research to be carried out for validationand development. Benefits to improving classification speed include improvingapplication in clinical or educational settings.

Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure