Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces
- Submitting institution
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University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 88641057
- Type
- D - Journal article
- DOI
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10.3389/fnins.2020.00918
- Title of journal
- Frontiers in Neuroscience
- Article number
- 918
- First page
- 1
- Volume
- 14
- Issue
- -
- ISSN
- 1662-4548
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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A - Intelligent Systems Research Centre
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <24> This was part of the first author’s PhD work, which was supported by the Department of Science and Technology India and UK India Education and Research Initiative Thematic Partnership project, 'Advancing MEG based BCI Supported Upper Limb Post-Stroke Rehabilitation' (DST-UKIERI-2016-17-0128). The developed CNN deep learning framework is planned to be used in designing a calibration-free BCI algorithm which will further enhance the post-stroke neuro-rehab system reported in an impact case study (REF2021) and make it a home usable device operable without professional support.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -