PUB - Publikationen an der Universität Bielefeld

An important step for the correlation of EEG signals with cognitive processes is the identification of discriminative features in the EEG signal. In this paper we utilize independent component analysis (ICA) for feature extraction and selection. Our specific ICA technique is based on a nonparametric source representation which in particular allows for modelling of multimodal feature distributions as generally required for the analysis of mixed data from different experiment conditions. To demonstrate the potential of the resulting ICA feature selection scheme we report results from an analysis of psycholinguistic experiments on the discrimination of speech perception from perception of so-called pseudo speech signals and demonstrate how the obtained ICA features can be further analyzed with the technique of sonification. Our results correlate well with results from coherence analysis and strongly indicate that these new methods are well suited for uncovering cognitively relevant features in EEG signals.