Improved Time-Frequency Features and Electrode Placement for EEG-Based Biometric Person Recognition
- Submitting institution
-
The University of Kent
- Unit of assessment
- 12 - Engineering
- Output identifier
- 16830
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2910752
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 49604
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- URL
-
https://kar.kent.ac.uk/73406/
- 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
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper’s significance is derived from its two key contributions. It proposes a model for the optimization of electrode positioning allowing systems to need far fewer electrodes together with a more efficient EEG feature representation to produce better classification accuracy. The scheme is applicable to other EEG applications such as BCI, Clinical diagnostics, etc. as well as in the processing of other bio-signals.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
