Localized component filtering for electroencephalogram artifact rejection

Localized component filtering for electroencephalogram artifact rejection.

Blind source separation (BSS) based artifact rejection systems have been extensively studied in the electroencephalogram (EEG) literature. Although there have been advances in the development of techniques capable of dissociating neural and artifactual activity, these are still not perfect. As a result, a compromise between reduction of noise and leakage of neural activity has to be found. Here...

Automated Statistical Thresholding for EEG artifact Rejection

Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection ...

Kurtosis, Renyi’s Entropy and Independent Component Scalp Maps for the Automatic Artifact Rejection from EEG data

The goal of this work is to improve the efficiency and the reliability of the automatic artifact rejection, in particular from the Electroencephalographic (EEG) recordings. Artifact rejection is a key topic in signal processing. The artifacts are unwelcome signals that may occur during the signal acquisition and that may alter the analysis of the signals themselves. A technique for the automati...

Independent Component Analysis of Electroencephalogram

The analysis of electroencephalographic (EEG) recording is important both for brain research and for medical diagnosis and treatment. Independent Component Analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from the EEG recordings. Results show that ICA is a useful technique for the evaluation of different variables in the brain activity.

Adaptive Filtering of Stimulus Artifact