Prof. Mario Chavez
CNRS UMR-7225, Hôpital Pitié-Salpêtrière, Paris, France
Detecting dynamic spatial correlation with generalized wavelet coherence
and non-stationary surrogate data
Time series measured from real-world systems are generally noisy, complex and display statistical properties that evolve continuously over time. Here, we present a method that combines wavelet analysis and non-stationary surrogates to detect short-lived spatial coherent patterns from multivariate time-series. In contrast with standard methods, the surrogate data used here are realisations of a non-stationary stochastic process, preserving both the amplitude and time-frequency distributions of original data. We evaluate this framework on synthetic and real-world time series, and we show that it can provide useful insights into the time-resolved structure of spatially extended systems.
Mario Chavez has a background in complex systems applied to neurosciences (M.S. and Ph.D. degrees in France). After holding different postdoctoral positions (France & Italy) in the field of nonlinear physics and biomedical signal processing, he became a researcher at Centre National de la Recherche Scientifique (CNRS). His research activities concern new methodologies for characterising functional connectivity of electrophysiological signals recorded at multiple scales (LFP/MEG/EEG/SEEG/fMRI). He has developed a complex network-based framework to quantify the functional interactions between different neural structures involved in generation and propagation of epileptic activities.
More information on the research group can be found at: http://charpierlab.fr
*Este coloquio se llevará a cabo el viernes 14/12 a las 14 hs en el Aula Seminarios del Dpto. de Física, 2º piso pab. I , Ciudad Universitaria.