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Subspace techniques for task-independent EEG person identification
Name
EMBC_Subject_ID.pdf
Description
Accepted version
Size
279.17 KB
Format
Adobe PDF
Checksum (MD5)
5145e4670b0267cc025faf05a69d8d0e
Author(s)
Kumar, Mari Ganesh
Saranya, MS
Narayanan, Shrikanth
Sur, Mriganka
Murthy, Hema A
Date Issued
2019
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kumar, Mari Ganesh, Saranya, MS, Narayanan, Shrikanth, Sur, Mriganka and Murthy, Hema A. 2019. "Subspace techniques for task-independent EEG person identification." Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019.
Version
Author's final manuscript
Abstract
© 2019 IEEE. There has been a growing interest in studying electroencephalography signals (EEG) as a possible biometric. The brain signals captured by EEG are rich and carry information related to the individual, tasks being performed, mental state, and other channel/measurement noise due to session variability and artifacts. To effectively extract person-specific signatures present in EEG, it is necessary to define a subspace that enhances the biometric information and suppresses other nuisance factors. i-vector and x-vector are state-of-art subspace techniques used in speaker recognition. In this paper, novel modifications are proposed for both frameworks to project person-specific signatures from multi-channel EEG into a subspace. The modified i-vector and x-vector systems outperform baseline i-vector and x-vector systems with an absolute improvement of 10.5% and 15.9%, respectively.
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/EMBC.2019.8857426
