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 …

Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive
Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly …

In this paper, we provide guidance for the organization and implementation of EEG studies.
This work was inspired by our experience conducting a large‐scale, multi‐site study, but …

Sleep is an important research area in nutritional medicine that plays a crucial role in human
physical and mental health restoration. It can influence diet, metabolism, and hormone …

Objective: To remove signal contamination in electroencephalogram (EEG) traces coming
from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real …

Electroencephalography (EEG) is a non-invasive method used to track human brain activity
over time. The time-locked EEG to an external event is known as event-related potential …

Objective. Automated artefact detection in the neonatal electroencephalogram (EEG) is
crucial for reliable automated EEG analysis, but limited availability of expert artefact …

Ocular artifact, namely eye-blink artifact, is an inevitable and one of the most destructive
noises of EEG signals. Many solutions of detecting the eye-blink artifact were proposed …

Electroencephalography (EEG) is a non-intrusive method used to capture electrical potential
generated by brain neurons, which is crucial for diagnosing neurological disorders like …

We introduce a novel approach to neural learning: the Feature-Imitating-Network (FIN). A
FIN is a neural network with weights that are initialized to reliably approximate one or more …