Improved Time-Frequency Features and Electrode Placement for EEG-Based Biometric Person Recognition
- Submitting institution
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The University of West London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11031
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2910752
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 49604
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2019
- URL
-
-
- 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
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This study introduces a novel feature extraction method for biometric recognition using EEG data and provides an analysis of the impact of electrode placements on performance. The proposed method compares favourably with other published reports while using a significantly smaller number of electrodes. The performance of the proposed system also showed substantial improvements in the verification scenario, when compared with some similar systems from the published literature. It is found that the proposed feature is less influenced by time separation between training and testing compared with a conventional feature based on power spectral analysis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -