Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller
- Submitting institution
-
University of Glasgow
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-09864
- Type
- D - Journal article
- DOI
-
10.1088/1741-2560/11/3/036003
- Title of journal
- Journal of Neural Engineering
- Article number
- -
- First page
- 36003
- Volume
- 11
- Issue
- 3
- ISSN
- 1741-2560
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
http://eprints.gla.ac.uk/93305/
- 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
-
9
- Research group(s)
-
-
- Citation count
- 40
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- ORIGINALITY: The first paper to combine efficient entropy-coded text entry for brain-computer interfaces with an infrequent undo and breaks new ground in robust algorithms for fusing low-capacity input devices. It is the first to clinically test this with impaired subjects. RIGOUR: An extensive clinical evaluation with both able-bodied and severely motor impaired participants, supported by an original, rigorous mathematical model of an infrequent undo channel, verified against empirical results. SIGNIFICANCE: This paper has major implications in helping motor-impaired individuals communicate. It sets out a methodology for future studies in text entry for impaired subjects, and has been highly cited.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -