  Brain signals could be used to control devices to assist individuals with
disabilities. Signals such as electroencephalograms are complicated and hard to
interpret. A set of signals are collected and should be classified to identify
the intention of the subject. Different approaches have tried to reduce the
number of channels before sending them to a classifier. We are proposing a deep
learning-based method for selecting an informative subset of channels that
produce high classification accuracy. The proposed network could be trained for
an individual subject for the selection of an appropriate set of channels.
Reduction of the number of channels could reduce the complexity of
brain-computer-interface devices. Our method could find a subset of channels.
The accuracy of our approach is comparable with a model trained on all
channels. Hence, our model's temporal and power costs are low, while its
accuracy is kept high.
