The volume of network and Internet traffic is expanding daily, with data being created at the
zettabyte to petabyte scale at an exceptionally high rate. These can be characterized as big …

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 …

In recent years, brainprint recognition has emerged as a novel method of personal identity
verification. Although studies have demonstrated the feasibility of this technology, some …

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

In this research paper, we propose an unsupervised framework for feature learning based
on an autoencoder to learn sparse feature representations for EEG-based person …

This paper proposes a novel method and algorithms for the design of MRI structured
personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling …

Over the years, the usage of artificial intelligence (AI) algorithms is increased to develop
various smart applications using Internet-of-Things. Home automation is a fast emerging …

Automatic speech recognition interfaces are becoming increasingly pervasive in daily life as
a means of interacting with and controlling electronic devices. Current speech interfaces …

The brain activity of a person sensed using electroencephalograph (EEG) has a unique
neuronal signature consisting of relevant subject‐specific information, which can be used for …

Electroencephalograph (EEG) based biometric identification has recently gained increased
attention of researchers. However, state-of-the-art EEG-based biometric identification …