Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm
- Submitting institution
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Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D2032
- Type
- D - Journal article
- DOI
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10.1109/TCBB.2016.2616395
- Title of journal
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Article number
- -
- First page
- 38-45
- Volume
- 15
- Issue
- 1
- ISSN
- 1545-5963
- Open access status
- Not compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
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-
- 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
-
-
- Research group(s)
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-
- Citation count
- 32
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The research focused on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The paper has received 41 citations and 759 reads as of 11/2/2021. The paper has become one of the standard references when researches highlight the benefits of using EEG in relation to the measurement of attention levels while subjects are forming learning tasks. Examples DOI:10.1088/1742-6596/1694/1/012013, DOI:10.1109/JSEN.2019.2962874.
Techniques used in the paper, particularly correlation-based feature selection (CFS) and use of the kNN algorithm, are also commonly referenced as successful examples. Examples: DOI:10.1515/freq-2019-0210, DOI: 10.1109/ACCESS.2020.2994985.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
