Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace Techniques
| Autor: | Shrikanth S. Narayanan, Mari Ganesh Kumar, Hema A. Murthy, Mriganka Sur |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Spoofing attack Biometrics Computer Networks and Communications Computer science 0211 other engineering and technologies 02 engineering and technology Electroencephalography Machine Learning (cs.LG) Task (project management) FOS: Electrical engineering electronic engineering information engineering medicine Electrical Engineering and Systems Science - Signal Processing Safety Risk Reliability and Quality 021110 strategic defence & security studies medicine.diagnostic_test business.industry Pattern recognition Speaker recognition ComputingMethodologies_PATTERNRECOGNITION Task analysis Artificial intelligence High-dimensional statistics business Subspace topology |
| Zdroj: | IEEE Transactions on Information Forensics and Security. 16:2856-2871 |
| ISSN: | 1556-6021 1556-6013 |
| DOI: | 10.1109/tifs.2021.3067998 |
| Popis: | 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. Comment: \copyright 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Databáze: | OpenAIRE |
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