showing 7 related works from this author
BED: A new dataset for EEG-based biometrics
2021
Various recent research works have focused on the use of electroencephalography (EEG) signals in the field of biometrics. However, advances in this area have somehow been limited by the absence of a common testbed that would make it possible to easily compare the performance of different proposals. In this work, we present a data set that has been specifically designed to allow researchers to attempt new biometric approaches that use EEG signals captured by using relatively inexpensive consumer-grade devices. The proposed data set has been made publicly accessible and can be downloaded from https://doi.org/10.5281/zenodo.4309471 . It contains EEG recordings and responses from 21 individuals…
Single-channel EEG-based subject identification using visual stimuli
2021
Electroencephalography (EEG) signals have been recently proposed as a biometrics modality due to some inherent advantages over traditional biometric approaches. In this work, we studied the performance of individual EEG channels for the task of subject identification in the context of EEG-based biometrics using a recently proposed benchmark dataset that contains EEG recordings acquired under various visual and non-visual stimuli using a low-cost consumer-grade EEG device. Results showed that specific EEG electrodes provide consistently higher identification accuracy regardless of the feature and stimuli types used, while features based on the Mel Frequency Cepstral Coefficients (MFCC) provi…
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…
EEG-based biometrics: effects of template ageing
2020
This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vecto…
Artificial intelligence for affective computing : an emotion recognition case study.
2020
This chapter provides an introduction on the benefits of artificial intelligence (Al) techniques for the field of affective computing, through a case study about emotion recognition via brain (electroencephalography EEG) signals. Readers are first pro-vided with a general description of the field, followed by the main models of human affect, with special emphasis to Russell's circumplex model and the pleasur-arousal-dominance (PAD) model. Finally, an AI-based method for the detection of affect elicited via multimedia stimuli is presented. The method combines both connectivity-and channel-based EEG features with a selection method that considerably reduces the dimensionality of the data and …
Image-Evoked Affect and its Impact on Eeg-Based Biometrics
2019
Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…
On the Influence of Affect in EEG-Based Subject Identification
2021
Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user’s current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the sign…