Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals
- Submitting institution
-
University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13058176
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2017.03.027
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 81
- Volume
- 244
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- March
- 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
-
2
- Research group(s)
-
-
- Citation count
- 28
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A novel method for affect detection is proposed that combines both connectivity-based and channel-based features with a selection method. It considerably reduces the dimensionality of the data and allows for an efficient classification. This work was invited by IET book “AI for emerging verticals; human-robot computing, sensing and networking”. The results of the paper helped us to create a new open access dataset (https://zenodo.org/record/4309472#.YDhXTuj7TIU) and will be published in IEEE IoT Journal. The proposed methodology is the main driving force to secure Innovate UK Grant #12215 with Kibble Education and Care Centre to develop affect detection system for Kibble residents.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -