学术预告--Time-varying statistical measures for studying brain functional connectivity
报告题目：Time-varying statistical measures for studying brain functional connectivity
York University, Canada
Time-frequency analysis is a powerful tool for understanding non-stationary characteristics of data such as brain signals. It can not only reveal brain activities occurred in a single brain region but also be used to derive statistical measures, such as coherence, to describe functional dynamics between different brain regions. In this talk, we address the use of time-frequency analysis to estimate time-varying statistical measures and then apply them to investigate motor cortex activities under the multisource interference task.
Dr. Zhu is an Associate Professor at the Department of Mathematics and Statistics, York University, Canada. She holds a PhD in Applied Mathematics at the University of Waterloo and worked as a MS Society of Canada postdoctoral fellowship at the Seaman MR Centre at Foothills Hospital, University of Calgary between 2001 and 2004. Hongmei’s research interests are in the areas of time-frequency analysis, data analysis, numerical computations and their applications in real-world problems arisen from biomedicine and other industries.
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