Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining
Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Identification of discriminative features in the EEG
Intelligent Data Analysis
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Human information processing can be monitored by analysing cognitive evoked potentials (EP) measurable in the electro encephalogram (EEG) during cognitive activities. In technical terms, both visualization of multi-dimensional sequential data and unsupervised discovery of patterns within this multivariate set of real valued time series is needed. Our approach towards visualization is to discretize the sequences via vector quantization and to perform a Sammon mapping of the codebook. Instead of having to conduct a time-consuming search for common subsequences in the set of multivariate sequential data, a multiple sequence alignment procedure can be applied to the set of one-dimensional discrete time series. The methods are described in detail and results obtained for spatial and verbal information processing are shown to be statistically valid, to yield an improvement in terms of noise attenuation and to be well in line with psychophysiological literature.