Journal article 644 views 96 downloads
Task sensitivity in EEG biometric recognition
,
Farzin Deravi,
Sanaul Hoque
Pattern Analysis and Applications, Volume: 21, Issue: 1, Pages: 105 - 117
Swansea University Author:
Scott Yang
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Copyright: The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
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DOI (Published version): 10.1007/s10044-016-0569-4
Abstract
This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who perfo...
| Published in: | Pattern Analysis and Applications |
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| ISSN: | 1433-7541 1433-755X |
| Published: |
Springer Science and Business Media LLC
2018
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58935 |
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| Abstract: |
This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who performed four different motor movement/imagery tasks while their data were recorded. Training and test of the system was performed using a number of experimental protocols to establish if training with one type of task and tested with another would significantly affect the recognition performance. Also, experiments were conducted to evaluate the performance when a mixture of data from different tasks was used for training. The results suggest that performance is not significantly affected when there is a mismatch between training and test tasks. Furthermore, as the amount of data used for training is increased using a combination of data from several tasks, the performance can be improved. These results indicate that a more flexible approach may be incorporated in data collection for EEG-based biometric systems which could facilitate their deployment and improved performance. |
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| Keywords: |
EEG; Biometrics; Identification; Verification |
| College: |
Faculty of Science and Engineering |
| Issue: |
1 |
| Start Page: |
105 |
| End Page: |
117 |