Denoising of electroencephalographic signals by canonical correlation analysis and by second-order blind source separation

Piugie, Yris Brice Wandji; Tchiotsop, Daniel; Telem, Adélaïde Nicole Kengnou; Ngouonkadi, Elie Bertrand Megam

doi:10.1109/africon46755.2019.9133834

Cited by 3 publications

(4 citation statements)

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“…In a database sample composed of 110 users, time series composed of 378 features is represented by a matrix of size 28 × 28. The matrix is displayed with imagesc() 3 function in MatLab which display image with scaled colors. We finally have a 3D image on RGB format.…”

Section: A Signal Processing : Matrix Representationmentioning

confidence: 99%

“…The development of Information and Communication Technologies (ICT), as well as improvements in ambient intelligent technologies, such as sensors and smart phones, have led to the rapid development of smart environments [1], [2]. Considerable resources can be saved if sensors can help staff record and monitor users or automatically report any abnormal behavior [2], [3]. For example, in payment systems, in order to ensure the application of strong customer authentication, it is necessary to require adequate security features 1 based on authentication factors such as knowledge, possession, inherent or biometric factors [4].…”

Section: Introductionmentioning

confidence: 99%

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Keystroke Dynamics based User Authentication using Deep Learning Neural Networks

Piugie

Manno²,

Rosenberger

et al. 2022

2022 International Conference on Cyberworlds (CW)

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Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multiinstance system.

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Section: A Signal Processing : Matrix Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Keystroke Dynamics based User Authentication using Deep Learning Neural Networks

Piugie

Manno²,

Rosenberger

et al. 2022

2022 International Conference on Cyberworlds (CW)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The development of Information and Communication Technologies, as well as improvements in ambient intelligent technologies, such as sensors and smartphones, have led to the growth of smart environments [4, 5]. By using sensors, staff can save resources by recording and monitoring users or automatically reporting any unusual behaviour [4, 6, 7]. For instance, in payment systems, to ensure strong customer authentication, it is necessary to implement adequate security features based on authentication factors such as knowledge, possession, inherent, or biometric factors [8].…”

Section: Introductionmentioning

confidence: 99%

Deep features fusion for user authentication based on human activity

Piugie

Charrier

Manno³

et al. 2023

IET Biometrics

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The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal-to-image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach.

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“…The development of information and communication technologies (ICT), as well as improvements in ambient intelligent technologies, such as sensors and smart phones, have led to the rapid development of smart environments [1], [2]. An enormous amount of resources can be saved if sensors can help staff record and monitor patients or automatically report any abnormal behavior [3], [4] like depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%