EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers
- Submitting institution
-
Liverpool Hope University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- AKN55C
- Type
- D - Journal article
- DOI
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10.1109/TETCI.2017.2750761
- Title of journal
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Article number
- -
- First page
- 437-453
- Volume
- 1
- Issue
- 6
- ISSN
- 2471-285X
- Open access status
- Compliant
- Month of publication
- December
- 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
- Yes
- Number of additional authors
-
2
- Research group(s)
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S - Spatial Computing and Robotics (SC&R)
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work was initiated during Nagar's sabbatical visit on a fully-funded Indian Government's "Human Resource Development in Mathematics” grant jointly supported by the Science and Engineering Research Board (SERB) and DST-India; grant No. SR/S4/MS:559/08.); 2013/14.
Online detection of cognitive failures to alert the driver by audio means to prevent possible accidents by automatic decoding of brain signals is useful for driving learners and careless/unmindful drivers. Field trials undertaken at Jadavpur University show promising results, and attempts are endeavored to realize the said neuro-fuzzy engine for the next generation commercial vehicles.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
