Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Discovery of Common Subsequences in Cognitive Evoked Potentials
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Modelling Discrete Event Sequences as State Transition Diagrams
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Detecting and Describing Patterns in Time-Varying Data Using Wavelets
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
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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 high 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.