Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1345
- Type
- D - Journal article
- DOI
-
10.1109/tnsre.2019.2893949
- Title of journal
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Article number
- -
- First page
- 358
- Volume
- 27
- Issue
- 3
- ISSN
- 1534-4320
- Open access status
- Compliant
- Month of publication
- January
- 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
- No
- Number of additional authors
-
3
- Research group(s)
-
B - Brain Computer Interfaces and Neural Engineering (BCI-NE)
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Driver fatigue is a really important, extensively studied, problem. Our work proposed new dynamic connectivity evaluation methods; a global metric for measuring overall synchronization.Published in a leading journal, the proposed methods were rigorously evaluated by a two-session driving experiment with a large cohort of people, demonstrating the novelty and advantage of capturing topological connections. Significantly these methods are also applicable to a wide range of studies exploring the dynamic properties of brain connectivity i.e. researchers can now investigate brain connectivity from the new view of dynamic high-order connectivity. Other than citations the work led to an invited-talk.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -