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

TL;DR

This paper introduces a mutual information-based method for EEG channel selection that accelerates classification speed, especially when using signal epochs, with potential benefits for real-time clinical and educational applications.

Contribution

It proposes a novel channel selection approach using mutual information and signal epochs to enhance EEG classification speed, balancing accuracy and efficiency.

Findings

Speed-up gains achieved with MI techniques

Using signal epochs improves classification efficiency

Trade-off between accuracy and speed demonstrated

Abstract

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.