EEG dimensionality reduction in automatic identification of synonymy

Recent work has demonstrated the feasibility of extracting semantic categories directly from cortical measures (e.g., electroencephalography, EEG) during receptive tasks. Here, we automatically classify speech stimuli as either synonymous or non-synonymous with a prior prime in a speech-receptive task given only EEG data with up to 86.84% accuracy. An analysis of variance reveals no significant difference among support vector machine and k-nearest neighbours classifiers, but a significant effect of the individual subject on accuracy. To perform classification, we reduce the highly-parameterized space by three successive techniques: a ranking based on t-test similarity, another based on principal components analysis (PCA), and a third on linear discriminant analysis.