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

Asquith, Phoebe M and Ihshaish, Hisham 2019. Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure. Presented at: 23rd International Database Engineering & Applications Symposium, Athens, Greece, 10-12 Jun 2019. Proceedings of the 23rd International Database Applications & Engineering Symposium. ACM, p. 5. 10.1145/3331076.3331095

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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.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Psychology
Publisher: ACM
ISBN: 9781450362498
Date of First Compliant Deposit: 11 November 2019
Date of Acceptance: 1 June 2019
Last Modified: 12 Nov 2019 14:15
URI: http://orca.cf.ac.uk/id/eprint/126713

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