Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance
- Submitting institution
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Nottingham Trent University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 24 - 700281
- Type
- D - Journal article
- DOI
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10.1088/1741-2552/aa814b
- Title of journal
- Journal of Neural Engineering
- Article number
- 66003
- First page
- -
- Volume
- 14
- Issue
- 6
- ISSN
- 1741-2560
- Open access status
- Deposit exception
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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-
- 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
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2
- Research group(s)
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A - Imaging, Materials and Engineering Centre
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper applied the new concept of neurovascular features in machine learning to non-invasive, hybrid brain activity data, and showed that this leads to higher classification accuracy and a way to assess the brain's neurovascular integrity. Building on the work, Dr Omurtag and Indus Instruments in Houston, TX (President: Dr Sridhar Madala, sridhar@indusinstruments.com) have collaborated to design a prototype device currently undergoing pilot testing.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -