The identification and classification of human brain activities are essential for many medical
and Brain-Computer Interface (BCI) systems, saving human lives and time …

As it was mentioned in the previous part of this work (Part I)—the advanced signal
processing methods are one of the quickest and the most dynamically develo** scientific …

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

Brain–computer interfaces (BCIs) suffer from a lack of classification accuracy when the
number of electroencephalography (EEG) trials is low. This is therefore during the learning …

Abstract Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a
significant challenge. Misdiagnosis has significant negative medical side effects. Due to the …

This study aimed at develo** a noncontact authentication system using event-related
pupillary response (ErPR) epochs in an augmented reality (AR) environment. Thirty …

The study of Electroencephalogram (EEG)-based biometric has gained the attention of
researchers due to the neurons' unique electrical activity representation of an individual …

Ensemble learners and deep neural networks are state-of-the-art schemes for classification
applications. However, deep networks suffer from complex structure, need large amount of …

A new authentication method based on EEG signal is proposed here. Biometric features
such as fingerprint scanning, facial recognition, iris scanning, voice recognition, and even …

Background: The precise differentiation of schizophrenic patients with positive and negative
symptoms is still challenging; hence, psychiatrists mainly focus on diagnosing schizophrenic …