报告主题：图分数阶全变分的脑电图源定位
报告人：Jing Qin assistant professor (Montana State University）
报告时间：2017年 6月22日（周四）8:00
报告地点：校本部G507
邀请人：彭亚新
报告摘要：EEG source imaging is able to reconstruct sources on the brain from scalp measurements with high temporal resolution. Due to the limited number of sensors, it is very challenging to locate the source accurately with high spatial resolution. Recently, several total variation (TV) based methods have been proposed to explore sparsity of the source spatial gradients, which is based on the assumption that the source is constant at each sub-region. However, since the sources have more complex structures in practice, these methods have difficulty in recovering the current density variation and locating source peaks. To overcome this limitation, we propose a graph Fractional-Order Total Variation (gFOTV) based method, which provides the freedom to choose the smoothness order by imposing sparsity of the spatial fractional derivatives so that it locates source peaks accurately.
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