EEuGene: employing electroencephalograph signals in the rating strategy of a hardware-based interactive genetic algorithm
- Submitting institution
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Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 811
- Type
- E - Conference contribution
- DOI
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10.1007/978-3-319-47175-4_25
- Title of conference / published proceedings
- Research and Development in Intelligent Systems XXXIII: Incorporating Applications and Innovations in Intelligent Systems XXIV
- First page
- 343
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- November
- Year of publication
- 2016
- URL
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http://eprints.mdx.ac.uk/20424/
- 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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1
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work explored sound synthesis using an interactive genetic algorithm. Traditionally, these algorithms utilize human input as a rating strategy by means of a slider or rating value. The importance and value of this work lies in the novel use of Employing Electroencephalograph signals for obtaining a rating value. It is significant because this approach has since been applied to new domains and is being used to generate artwork and to assist people in reducing stress levels as demonstrated at the 2018 BCS Machine Intelligence competition.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -