Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure
This incremental work addresses the need for faster EEG classification in clinical or educational settings, though it is exploratory and requires further validation.
The paper tackled the problem of efficient EEG channel selection for online classification by introducing a Mutual Information (MI) method, achieving promising speed-up gains with a penalty on classification accuracy scores, and using signal epochs (3 seconds) containing transitions to enhance these gains.
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.