Novel trends in affective computing are based on reliable sources of physiological signals
such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin …

This study focuses on using EEG signal-based behavioral biometric data to classify and
identify persons. A person identification system based on a nonlinear model with excellent …

Electroencephalography (EEG) based biometric systems are gaining attention for their anti-
spoofing capability but lack accuracy due to signal variability at different psychological and …

Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has
become a potential field of research in recent years. Although several studies have been …

Biometrics have attracted growing research interests as information security and safety gain
increasing attention to date. As a kind of important biomedical signal, electroencephalogram …

Owing to the increased public interest in passive brain–computer interface (pBCI)
applications, many wearable devices for capturing electroencephalogram (EEG) signals in …

Searching for new biometric traits is currently a necessity because traditional ones such as
fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric …

Association between electroencephalography (EEG) and individually personal information
is being explored by the scientific community. Though person identification using EEG is an …

Biometric authentication systems have recently been gaining increased attention as an
integral part of modern civic life. Security surveillance systems encompass broad categories …

Electrocardiogram (ECG) is the measure of the electrical activity of heart which provides a
powerful authentication system for person recognition due to the variation in ECG …