Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …

Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

Generative adversarial networks (GANs) are recently highly successful in generative
applications involving images and start being applied to time series data. Here we describe …

Abstract Machine learning (ML) methods have the potential to automate clinical EEG
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …

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 …

Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …

Classification of electroencephalography (EEG) signals corresponding to imagined speech
production is important for the development of a direct-speech brain–computer interface (DS …

A revised computational model of circadian phototransduction is presented. The first step
was to characterize the spectral sensitivity of the retinal circuit using suppression of the …

Abstract Machine Learning has recently found a fertile ground in EMG signal decoding for
prosthesis control. However, its understanding and acceptance are strongly limited by the …