Affective brain–computer music interfacing
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1165
- Type
- D - Journal article
- DOI
-
10.1088/1741-2560/13/4/046022
- Title of journal
- Journal of Neural Engineering
- Article number
- 046022
- First page
- 046022
- Volume
- 13
- Issue
- 4
- ISSN
- 1741-2552
- Open access status
- Technical exception
- Month of publication
- July
- Year of publication
- 2016
- 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
-
7
- Research group(s)
-
B - Brain Computer Interfaces and Neural Engineering (BCI-NE)
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Originality: This paper builds upon highly-cited papers (Daly, 2014, 2014b, 2015). It combines state-of-the-art analysis, music-generation, and machine-learning to produce the world's first affective brain-computer music interface.
Significance: With >1200 downloads and >10k dataset accesses this work was reported in a widely-read article on the Guardian (https://www.theguardian.com/lifeandstyle/2016/sep/12/mind-blowing-music-tinie-tempahs-brain-scan). This is the first demonstration of how a BCMI may be used in a therapeutic context (later corroborated with Huntingon's patients). This led to advances in music-streaming for depression (Schriewer-USA), and investigations of brain-music cross-correlations (Lin, UoC-USA).
Rigour: This was a large longitudinal study looking at how to train aBCMIs with multiple users.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -