Efficient and Accurate Neural Fingerprints Obtained via Mean Curve Length of High Dimensional Model Representation of EEG Signals

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Tarih

2023

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

European Signal Processing Conference, EUSIPCO

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

In this study, we propose and evaluate a feature extraction methodology for the purpose of EEG-based person recognition. To this end, the mean curve length (MCL) was employed subsequent to the representation of EEG signals in an orthogonal geometry through High Dimensional Model Representation (HDMR). To analyze the effectiveness of the methodology, we executed it on a standard publicly available EEG dataset containing 109 subjects and acquired from 64 channels for eyes-open (EO) and eyes-closed (EC) resting-state conditions. The proposed feature was evaluated by comparing it to MCL, beta, and gamma band activities. According to the performance results, applying MCL to the output of the HDMR instead of raw data provides superior performances for identification and authentication. The attained results promise a novel simple, fast, and accurate biometric recognition scheme, named HDMRMCL. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

Açıklama

31st European Signal Processing Conference, EUSIPCO 2023 -- 4 September 2023 through 8 September 2023 -- -- 194070

Anahtar Kelimeler

authentication, biometrics, EEG, HDMR, identification, mean curve length, resting-state

Kaynak

European Signal Processing Conference

WoS Q Değeri

Scopus Q Değeri

N/A

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