Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysis

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Event-related potential data can be used to index perceptual and cognitive operations. However, they are typically high-dimensional and noisy. This study examines the original raw data and six feature-extraction methods as a pre-processing step before classification. Four traditionally used feature-extraction methods were considered: principal component analysis, independent component analysis, auto-regression, and wavelets. We add to these a less well-known method called interval feature extraction. It overproduces features from the ERP signal and then eliminates irrelevant and redundant features by the fast correlation-based filter. To make the comparisons fair, the other feature-extraction methods were also run with the filter. An experiment on two EEG datasets (four classification scenarios) was carried out to examine the classification accuracy of four classifiers on the extracted features: support vector machines with linear and perceptron kernel, the nearest neighbour classifier and the random forest ensemble method. The interval features led to the best classification accuracy in most of the configurations, specifically when used with the Random Forest classifier ensemble.