Robustness of Mutual Information to Inter-Subject Variability for Automatic Artefact Removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as brain-computer interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of mutual information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a support vector machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved