A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
- Submitting institution
-
Edinburgh Napier University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2415506
- Type
- D - Journal article
- DOI
-
10.1016/j.neunet.2019.12.006
- Title of journal
- Neural Networks
- Article number
- -
- First page
- 176
- Volume
- 123
- Issue
- -
- ISSN
- 0893-6080
- Open access status
- Not compliant
- Month of publication
- December
- Year of publication
- 2019
- 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
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 25
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is a pioneering study conducted with international collaborators in Italy, that introduced higher-order-statistics (HOS) features to effectively account for multi-variate non-stationarity and nonlinearity effects in EEG recordings. The developed framework is generic, and is being extended and applied in a range of clinical applications, e.g. for epileptic seizure detection (https://doi.org/10.1016/j.irbm.2020.06.008), and emotional states recognition (https://doi.org/10.1007/s12559-020-09789-3). The multi-modal learning approach has also underpinned our EPSRC programme grant (COG-MHEAR:EP/T021063/1) for transformative wireless RF based lip-reading, and monitoring of enduser cognitive load and listening effort in future multi-modal hearing aids and assistive technology.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
