This paper presents an extensive review on the artifact removal algorithms used to remove
the main sources of interference encountered in the electroencephalogram (EEG) …

Over the last ten years blind source separation (BSS) has become a prominent processing
tool in the study of human electroencephalography (EEG). Without relying on head modeling …

Group independent component analysis (ICA) has been widely applied to studies of multi-
subject fMRI data for computing subject specific independent components with …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system; neural networks provide a model …

This paper compares different ICA preprocessing algorithms on cross-validated training data
as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over …

Here we present a robust physiological model for the recognition of human emotions, called
Deep Physiological Affect Network. This model is based on a convolutional long short-term …

Independent component analysis (ICA) utilizing prior information, also called semiblind ICA,
has demonstrated considerable promise in the analysis of functional magnetic resonance …

OBJECTIVE: To devise an automated system to remove artifacts from ictal scalp EEG, using
independent component analysis (ICA). METHODS: A Bayesian classifier was used to …

What is disclosed is a system and method for processing a time-series signal generated by
video images captured of a Subject of interest in a non-contact, remote sensing environ …

In multicell multiuser massive multi-input multi-output (MIMO) systems, pilot contamination
degrades the uplink (UL) channel estimation performance. To mitigate the effect of pilot …