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

TL;DR

This paper introduces a deep learning method for selecting the most informative EEG channels for subject intention classification, reducing device complexity while maintaining high accuracy.

Contribution

It proposes a novel deep learning-based approach for individual-specific EEG channel selection that preserves classification accuracy with fewer channels.

Findings

Achieves comparable accuracy with fewer EEG channels

Reduces complexity and power consumption of brain-computer interfaces

Effective for individual-specific channel selection

Abstract

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.