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 murine electroencephalography (EEG) data during sleep transitions.
Original language | English |
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Title of host publication | 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Place of Publication | New York |
Publisher | IEEE Press |
Pages | 5931-5934 |
Number of pages | 4 |
ISBN (Print) | 978-1-4244-4122-8 |
Publication status | Published - 2011 |
Event | 33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) - Boston Duration: 30 Aug 2011 → 3 Sept 2011 |
Conference
Conference | 33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) |
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City | Boston |
Period | 30/08/11 → 3/09/11 |