Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace Techniques
Name
2007.13517.pdf
Description
Accepted version
Size
1.64 MB
Format
Adobe PDF
Checksum (MD5)
9122e610b7e7390b52adbb15e90cb11a
Author(s)
Kumar, Mari Ganesh
Narayanan, Shrikanth
Sur, Mriganka
Murthy, Hema A
Date Issued
2021
Journal
IEEE Transactions on Information Forensics and Security
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kumar, Mari Ganesh, Narayanan, Shrikanth, Sur, Mriganka and Murthy, Hema A. 2021. "Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace Techniques." IEEE Transactions on Information Forensics and Security, 16.
Version
Author's final manuscript
Abstract
Electroencephalography (EEG) signals are promising as alternatives to other biometrics owing to their protection against spoofing. Previous studies have focused on capturing individual variability by analyzing task/condition-specific EEG. This work attempts to model biometric signatures independent of task/condition by normalizing the associated variance. Toward this goal, the paper extends ideas from subspace-based text-independent speaker recognition and proposes novel modifications for modeling multi-channel EEG data. The proposed techniques assume that biometric information is present in the entire EEG signal and accumulate statistics across time in a high dimensional space. These high dimensional statistics are then projected to a lower dimensional space where the biometric information is preserved. The lower dimensional embeddings obtained using the proposed approach are shown to be task-independent. The best subspace system identifies individuals with accuracies of 86.4% and 35.9% on datasets with 30 and 920 subjects, respectively, using just nine EEG channels. The paper also provides insights into the subspace model's scalability to unseen tasks and individuals during training and the number of channels needed for subspace modeling.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TIFS.2021.3067998
