Efficient feature selection for electroencephalogram-based authentication

Alzahab, Nibras Abo; Baldi, Marco; Scalise, Lorenzo

doi:10.1109/memea52024.2021.9478700

Cited by 8 publications

(1 citation statement)

References 25 publications

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“…Following the EEG duration experiment conducted in [12], the EEG-epoch was split into 4-second segments; By EEG-epoch we mean a specific timewindows extracted from the continuous EEG signal. Instead, the features were extracted from the cluster map dataset in accordance with the results of a previous finding [6]. Three distinct classifiers were employed for classification: Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and eXtream Gradient Boosting (XGBoost).…”

Section: B Auditory Stimuli Analysismentioning

confidence: 99%

Effect of Auditory Stimuli on Electroencephalography-based Authentication

Alzahab¹,

Iorio²,

Baldi³

et al. 2022

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Opposed to standard authentication methods based on credentials, biometric-based authentication has lately emerged as a viable paradigm for attaining rapid and secure authentication of users. Among the numerous categories of biometric traits, electroencephalogram (EEG)-based biometrics is recognized as a promising method owing to its unique characteristics. This paper provides an experimental evaluation of the effect of auditory stimuli (AS) on EEG-based biometrics by studying the following features: i) general change in AS-aided EEG-based biometric authentication in comparison with non-AS-aided EEG-based biometric authentication, ii) role of the language of the AS and ii) influence of the conduction method of the AS. Our results show that the presence of an AS can improve authentication performance by 9.27%. Additionally, the performance achieved with an in-ear AS is better than that obtained using a bone-conducting AS. Finally, we verify that performance is independent of the language of the AS. The results of this work provide a step forward towards designing a robust EEG-based authentication system.

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Section: B Auditory Stimuli Analysismentioning

confidence: 99%