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Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning

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Document pages: 5 pages

Abstract: Brain signals could be used to control devices to assist individuals withdisabilities. Signals such as electroencephalograms are complicated and hard tointerpret. A set of signals are collected and should be classified to identifythe intention of the subject. Different approaches have tried to reduce thenumber of channels before sending them to a classifier. We are proposing a deeplearning-based method for selecting an informative subset of channels thatproduce high classification accuracy. The proposed network could be trained foran individual subject for the selection of an appropriate set of channels.Reduction of the number of channels could reduce the complexity ofbrain-computer-interface devices. Our method could find a subset of channels.The accuracy of our approach is comparable with a model trained on allchannels. Hence, our model s temporal and power costs are low, while itsaccuracy is kept high.

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