Wearable electronics is a rapidly growing field that recently started to introduce successful
commercial products into the consumer electronics market. Employment of biopotential …

Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a
challenge for emerging real-world EEG applications. Classic wet electrodes are the gold …

Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

Neurosciences and Neuro-technology are continuously advancing and so individuals,
society and healthcare professionals have to up date themselves with advancement. Brain …

Neuromarketing is the application of neuroscience to the understanding of consumer
preferences toward products and services. As such, it studies the neural activity associated …

In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly
on clinical applications, notably to enable severely disabled people to interact with the …

Recently, biometric data generated by fingerprints, hand geometry, heart rate, voice
patterns, facial characteristics and expressions, brain activity and body movement has …

During the last few years, there has been growing interest in the effects induced by
individual variability on activation patterns and brain connectivity. The practical implications …

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 …

Graph embedding is an effective method for deriving low-dimensional representations of
graph data. The power of graph deep learning methods to characterize …