TR2016-105
High-Accuracy User Identification Using EEG Biometrics
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- , "High-Accuracy User Identification Using EEG Biometrics", International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), DOI: 10.1109/EMBC.2016.7590835, August 2016, pp. 854-858.BibTeX TR2016-105 PDF Presentation
- @inproceedings{Koike-Akino2016aug,
- author = {Koike-Akino, Toshiaki and Mahajan, Ruhi and Marks, Tim K. and Tuzel, C. Oncel and Wang, Ye and Watanabe, Shinji and Orlik, Philip V.},
- title = {High-Accuracy User Identification Using EEG Biometrics},
- booktitle = {International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
- year = 2016,
- pages = {854--858},
- month = aug,
- doi = {10.1109/EMBC.2016.7590835},
- url = {https://www.merl.com/publications/TR2016-105}
- }
- , "High-Accuracy User Identification Using EEG Biometrics", International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), DOI: 10.1109/EMBC.2016.7590835, August 2016, pp. 854-858.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Machine Learning, Signal Processing
Abstract:
We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.