Abstract
Nowadays, data are recorded with increasing spatial and temporal resolution. Commonly these data are analyzed using univariate or bivariate approaches. Most of the analysis techniques assume stationarity of the underlying dynamical
processes. Here, we present an approach that is capable of analyzing multivariate data, the so-called renormalized partial directed coherence. It utilizes the concept of Granger causality and is applicable to non-stationary data. We discuss its abilities and limitations and demonstrate its usefulness in an application to sleep transitions in an animal model
processes. Here, we present an approach that is capable of analyzing multivariate data, the so-called renormalized partial directed coherence. It utilizes the concept of Granger causality and is applicable to non-stationary data. We discuss its abilities and limitations and demonstrate its usefulness in an application to sleep transitions in an animal model
Original language | English |
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Publication status | Published - 2011 |
Event | IEEE Engineering in Medicine and Biology Society 2011 - Boston, United States Duration: 30 Aug 2011 → 3 Sept 2011 |
Conference
Conference | IEEE Engineering in Medicine and Biology Society 2011 |
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Country/Territory | United States |
City | Boston |
Period | 30/08/11 → 3/09/11 |
Keywords
- EEG Engineering Medicine