EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices
- Submitting institution
-
Staffordshire University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 6794
- Type
- D - Journal article
- DOI
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10.1088/1741-2552/ab0dad
- Title of journal
- Journal of Neural Engineering
- Article number
- -
- First page
- 036016
- Volume
- 16
- Issue
- 3
- ISSN
- 1741-2560
- Open access status
- Deposit exception
- Month of publication
- April
- Year of publication
- 2019
- URL
-
https://iopscience.iop.org/article/10.1088/1741-2552/ab0dad
- 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
- Yes
- Number of additional authors
-
6
- Research group(s)
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B - Centre for Smart Systems, AI and Cybersecurity (CSSAIC)
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presented a new EEG representation that provides a more realistic view of brain functionality. The significance of this is that results demonstrate superior diagnosis of mental health conditions compared to existing methods. As a result of this work, the main author, Sadatnejad, secured a postdoctoral research position at INRIA Sud-Ouest, France in 2019. This paper also formed the basis of another paper in Channel Selection over Riemannian Manifold with Non-Stationarity Consideration for Brain-Computer Interface Applications, published in the IEEE International Conference on Acoustics, Speech and Signal Processing, 2020.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -