Approximate entropy for EEG-based movement detection

An approximate entropy feature is tested with parameters appropriate for online BCI - a short calculation window and use of the running standard deviation of the EEG signal. Features are extracted from self-paced real movement data, with various values of the embedding dimension and tolerance of comparison. Two alternative features, band power and reflection coefficients, are extracted for comparative purposes. Class separability is measured using classiffication results from k-means clustering for individual features and linear discriminant analysis for multiple features, as selected by sequential forward floating search. Results show this method of calculating approximate entropy to be a candidate for online movement detection in self-paced BCI systems.