Automatic Artifact Removal (AAR) toolbox for MATLAB. This MATLAB toolbox integrates several state-of-the-art methods for automatic removal of artifacts in the electroencephalogram (EEG). The methods implemented so far are only for removal of ocular (EOG) and muscular (EMG) artifacts. EOG removal methods include regression techniques based on Least Mean Squares (LMS), Recursive Least Squares (RLS) and other adaptive algorithms. However, the core functionality of the toolbox is a general-purpose artifact removal procedure that consists on three steps. First, the EEG data is decomposed into several spatial components using Blind Source Separation (BSS). Second, a suitable criteria is used to automatically detect artifact-related components. Third, the EEG data is reconstructed using only non-artifactual components. The toolbox is designed so that the user can easily expand it by adding new BSS algorithms and new criteria for detecting artifactual components. Furthermore it can be easily integrated as a plug-in into EEGLAB, which is a very popular graphical toolbox for EEG analysis and visualization in MATLAB.
References in zbMATH (referenced in 1 article )
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- Chen, Xun; Liu, Aiping; Wang, Z. Jane; Peng, Hu: Corticomuscular activity modeling by combining partial least squares and canonical correlation analysis (2013)