AlSaleh, M., Moore, R., Christensen, H. et al. (1 more author) (2018) Discriminating between imagined speech and non-speech tasks using EEG. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) , 18-21 Jul 2018, Honolulu, Hawaii. IEEE , pp. 1952-1955. ISBN 978-1-5386-3646-6
Abstract
People who are severely disabled (e.g Locked-in patients) need a communication tool translating their thoughts using their brain signals. This technology should be intuitive and easy to use. To this line, this study investigates the possibility of discriminating between imagined speech and two types of non-speech tasks related to either a visual stimulus or relaxation. In comparison to previous studies, this work examines a variety of different words with only single imagination in each trial. Moreover, EEG data are recorded from a small number of electrodes using a low-cost portable EEG device. Thus, our experiment is closer to what we want to achieve in the future as communication tool for locked-in patients. However, this design makes the EEG classification more challenging due to a higher level of noise and variations in EEG signals. Spectral and temporal features, with and without common spatial filtering, were used for classifying every imagined word ( and for a group of words) against the non-speech tasks. The results show the potential for discriminating between each imagined word and non-speech tasks. Importantly, the results are different between subjects using different features showing the need for having subject specific features.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Electroencephalography; Task analysis; Feature extraction; Time-domain analysis; Visualization; Support vector machines; Tools |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 May 2018 12:05 |
Last Modified: | 28 Nov 2018 14:40 |
Published Version: | https://doi.org/10.1109/EMBC.2018.8512681 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/EMBC.2018.8512681 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130004 |