The significance of deep learning techniques in relation to steady‐state visually evoked
potential‐(SSVEP‐) based brain‐computer interface (BCI) applications is assessed through …

Multi-modal fusion combines multiple modal information to overcome the limitation of
incomplete information expressed by a single modality, so as to realize the complementarity …

The exploitation of brain signals for biometric recognition purposes has received significant
attention from the scientific community in the last decade, with most of the efforts so far …

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 …

The scientific advances of recent years have made available to anyone affordable hardware
devices capable of doing something unthinkable until a few years ago, the reading of brain …

Electroencephalogram (EEG)-based authentication has received increasing attention from
researchers as they believe it could serve as an alternative to more conventional personal …

Recently, the use of Electroencephalography (EEG) in scientific research on User
Authentication (UA) has led to cutting-edge experiments that seek to identify and …

Improving system security can be achieved through people identification. Among various
methods, electroencephalography-based (EEG-based) identification is a dependable way to …

With the advent of consumer wearables that capture brain activity, the use of brainwaves to
verify a user's identity has been proposed as a convenient alternative to passwords. While …

An electroencephalogram (EEG) is a measurement that reflects the overall electrical activity
in the brain. EEG signals are effective for biometric authentication and robust against …