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EEG-based biometrics: phase-locking value from gamma band performs well across heterogeneous datasets

Pradeep Kumar, G and Dutta, U and Sharma, K and Ganesan, RA (2022) EEG-based biometrics: phase-locking value from gamma band performs well across heterogeneous datasets. In: 21st International Conference of the Biometrics Special Interest Group, BIOSIG 2022, 14 - 16 September 2022, Darmstadt.

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Official URL: https://doi.org/10.1109/BIOSIG55365.2022.9897042

Abstract

The performance of functional connectivity metrics is investigated for electroencephalogram (EEG)-based biometrics using a support vector machine classifier. Experiments are conducted on a heterogeneous EEG dataset of 184 subjects formed by pooling three distinct datasets recorded with different systems and protocols. The identification accuracy is found to be higher for higher frequency EEG bands, indicating the enhanced uniqueness of the neural signatures in beta and gamma bands. Using all the 56 EEG channels common to the three databases, the best identification accuracy of 97.4 is obtained using phase locking value-based measures extracted from the gamma frequency band. When the number of channels is reduced to 21 from 56, there is a marginal reduction of 2.4 only in the identification accuracy. Additional experiments are conducted to study the effect of the cognitive state of the subject and mismatched train/test conditions on the system performance. © 2022 IEEE.

Item Type: Conference Paper
Publication: BIOSIG 2022 - Proceedings of the 21st International Conference of the Biometrics Special Interest Group
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Biometrics; Support vector machines, Functional connectivity; Gamma band; Heterogeneous datasets; High frequency HF; Identification accuracy; Neural signatures; Performance; Phase-Locking values; Support vector machine classifiers; Support vectors machine, Electroencephalography
Department/Centre: Division of Biological Sciences > Centre for Neuroscience
Date Deposited: 16 Dec 2022 09:37
Last Modified: 16 Dec 2022 09:37
URI: https://eprints.iisc.ac.in/id/eprint/78473

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