Electroencephalogram (EEG), boasting the advantages of portability, low cost, and
hightemporal resolution, is a non-invasive brain-imaging modality that can be used to …

Removal of motion artifacts is a critical challenge, especially in wearable
electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed …

Electroencephalography (EEG) is the signal of intrigue that has immense application in the
clinical diagnosis of various neurological, psychiatric, psychological, psychophysiological …

Electroencephalogram (EEG) being the measure to record the electrical activity of brain acts
as a key factor to many brain computer interface (BCI) applications. These recorded EEG …

With the burgeoning of wearable devices and passive body/brain-computer interfaces
(B/BCIs), automated stress monitoring in everyday settings has gained significant attention …

Electroencephalogram (EEG) is a diagnostic test, and it measures the entire brain's
electrical activity. The EEG signals have been used in many applications such as the …

Classification is one of the main applications of machine learning, which can group and
classify the cases based on learning and development using the available data and …

The Electroencephalogram (EEG) recordings from the frontal lobe of the human brain help
in analyzing several important brain functions like motor functions, problem-solving skills …

Electroencephalography (EEG) signals are easily contaminated by various artifacts, making
noise removal an essential step in EEG analysis. In recent years, deep-learning-based …

Background Removing artifacts is a prerequisite step for the analysis of
electroencephalographic (EEG) signals. Artifacts appear in both time and time-frequency as …