Motivation Scientists increasingly rely on intelligent information systems to help them in their
daily tasks, in particular for managing research objects, like publications or datasets. The …

Objective: Interpretation of the electroencephalographic (EEG) and
magnetoencephalographic (MEG) signals requires off-line artifacts removal. Since artifacts …

Electroencephalogram (EEG) signals are widely applied in emotion recognition, sentiment
analysis, disease classification, sleep disorder identification, and fatigue detection. Recent …

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 …

EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond
traditional neurofeedback training, such as enabling personalized and optimized virtual …

We examine the utility of implicit user behavioral signals captured using low-cost, off-the-
shelf devices for anonymous gender and emotion recognition. A user study designed to …

In this paper, as a new application of machine learning, a K-Nearest Neighbor (KNN)
classification model is proposed to recognize artifacts in Electrocardiography (ECG) signal …

Electroencephalography (EEG) is a rich source of information regarding brain function.
However, the preprocessing of EEG data can be quite complicated, due to several factors …

EEG signals are complex and low-frequency signals. Therefore, they are easily influenced
by external factors. EEG artifact removal is crucial in neuroscience because artifacts have a …

Machine learning techniques such as deep learning have been increasingly used to assist
EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In …