The proposal to use brain connectivity as a biomarker for dementia phenotyping can be
potentiated by conducting large-scale multicentric studies using high-density …

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

The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data
for various brain-computer interface (BCI) applications. Despite numerous promising …

Segmentation of breast ultrasound images is a crucial and challenging task in computer-
aided diagnosis systems. Accurately segmenting masses in benign and malignant cases …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

The Brain-Computer Interface system is a profoundly developing area of experimentation for
Motor activities which plays vital role in decoding cognitive activities. Classification of …

It is well known that electroencephalograms (EEGs) often contain artifacts due to muscle
activity, eye blinks, and various other causes. Detecting such artifacts is an essential first …

Ocular artifacts, including blinks and saccades, pose significant challenges in the analysis of
electroencephalographic (EEG) data, often obscuring crucial neural signals. This tutorial …

Prior research has validated Photoplethysmography (PPG) as a promising biomarker for
assessing stress factors in construction workers, including physical fatigue, mental stress …

Objective. The quality of electroencephalogram (EEG) signals directly impacts the
performance of brain–computer interface (BCI) tasks. Many methods have been proposed to …