A Co-Adaptive Brain-Computer Interface for End Users with Severe Motor Impairment
- Submitting institution
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The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1333
- Type
- D - Journal article
- DOI
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10.1371/journal.pone.0101168
- Title of journal
- PLoS ONE
- Article number
- ARTN e101168
- First page
- e101168
- Volume
- 9
- Issue
- 7
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2014
- 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
-
5
- Research group(s)
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B - Brain Computer Interfaces and Neural Engineering (BCI-NE)
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to assess the effectivity of a spontaneous electroencephalogram (EEG)-based online brain-machine co-adaptive training paradigm in persons with severe motor impairments. Eighteen out of 22 disabled first-time BCI users achieved performances that were significantly better than chance in shorter time compared to the use of conventional approaches. Findings are significant as meaningful transfer of BCI technology into real applications was enabled. Paper builds upon previous work co-authored by R Scherer (IEEE TNSRE 20(3):313-319, 2012, IEEE TBME 54(3):550-556, 2007) and was further improved. Developed and assessed methods included and discussed in several review articles.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -