Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

Brain computer interface technology represents a highly growing field of research with
application systems. Its contributions in medical fields range from prevention to neuronal …

Motor imagery classification is an important topic in brain-computer interface (BCI) research
that enables the recognition of a subject's intension to, eg, implement prosthesis control. The …

What if we could effectively read the mind and transfer human visual capabilities to computer
vision methods? In this paper, we aim at addressing this question by developing the first …

Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …

A recent paper [1] claims to classify brain processing evoked in subjects watching ImageNet
stimuli as measured with EEG and to employ a representation derived from this processing …

This paper proposes an automated visual classification framework in which a novel analysis
method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the …

Recent advancements in generative adversarial networks (GANs), using deep convolutional
models, have supported the development of image generation techniques able to reach …

Predicting biomedical outcomes from Magnetoencephalography and
Electroencephalography (M/EEG) is central to applications like decoding, brain-computer …

Industrial Internet of Things (IIoT) and its applications have seen a paradigm shift since the
advent of artificial intelligence and machine learning. However, these methods are mostly …