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