Efficient and Accurate Neural Fingerprints Obtained via Mean Curve Length of High Dimensional Model Representation of EEG Signals
Küçük Resim Yok
Tarih
2023
Yazarlar
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
