Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
- Submitting institution
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The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1460272
- Type
- D - Journal article
- DOI
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10.1504/IJDMB.2017.086097
- Title of journal
- International Journal of Data Mining and Bioinformatics
- Article number
- -
- First page
- 1
- Volume
- 18
- Issue
- 1
- ISSN
- 1748-5673
- Open access status
- Not compliant
- Month of publication
- August
- Year of publication
- 2017
- 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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4
- Research group(s)
-
-
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Paper was substantially extended, by invitation, from our work (Li et al., 2016) in a top bioinformatics conference (BIBM2016). It is among the first deep learning structures for emotion recognition based on multiple physiological signals. It is recognised as a 10-year advance of deep learning in EEG analysis in terms of improving the classification performance and utilizing strengths of different models (Gong et al., 2020); as a state-of-the-art approach, e.g., (Wan et al., 2020); and as an experimental comparator (Chao et al., 2020). It is forming the basis of a current collaborative grant proposal with NHS Greater Glasgow and Clyde.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -