Automatic detection of EEG artefacts arising from head movements

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. In this paper, we present the results of an investigation into appropriate features for artefact detection in the REACT ambulatory EEG system. The study focuses on EEG artefacts arising from head movement. The use of one generalised movement artefact class to detect movement artefacts is proposed. Temporal, frequency, and entropy-based features are evaluated using Kolmogorov-Smirnov and Wilcoxon rank-sum non-parametric tests, Mutual Information Evaluation Function and Linear Discriminant Analysis. Results indicate good separation between normal EEG and artefacts arising from head movement, providing a strong argument for treating these head movement artefacts as one generalised class rather than treating their component signals individually.