Classifying EEG signals in Fisher discriminant spaces by random electrode selection

This paper introduces an ensemble approach for electroencephalogram (EEG) signal classification, which aims to overcome the instability of the Fisher discriminant feature extractor for brain-computer interface (BCI) applications. Through the random selection of electrodes from candidate electrodes, multiple individual classifiers are constructed. In a feature subspace determined by a couple of randomly selected electrodes, principal component analysis (PCA) is first used to implement dimensionality reduction. Successively Fisher discriminant is adopted for feature extraction, and a Bayesian classifier with a Gaussian mixture model (GMM) is trained to carry out classification. The outputs from all the individual classifiers are combined to give a final label. Experiments with real EEG signals taken from a BCI indicate the validity of the proposed random electrode selection (RES) approach.