Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works


Friday, April 13, 2012

Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs

Sometimes people asked me why I am interested in EEG/MEG source localization. In their eyes EEG/MEG source localization seems to be a direction with minor importance. However, when I researched in ICA with application to EEG/MEG data analysis before 2009, I realized the importance of EEG/MEG source localization, and this is why I started my research on sparse signal recovery in 2009.

I can spend more than 1000 words to detail the reasons. But the following paper well explained for me. Particularly, this nice paper implies the true value of EEG/MEG source localization study: the true value is not to pursuit higher spatial resolution, but serves as a crucial step for mining the brain connectivity. Here is the paper:

Satu Palva, J.Matias Palva, Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs, Trends in Cognitive Sciences, vol.16, no.4, 2012, pp.219-230

Abstract:
The systems-level neuronal mechanisms that coordinate temporally, anatomically and functionally distributed neuronal activity into coherent cognitive operations in the human brain have remained poorly understood. Synchronization of neuronal oscillations may regulate net- work communication and could thus serve as such a mechanism. Evidence for this hypothesis, however, was until recently sparse, as methodological challenges limit the investigation of interareal interactions with non- invasive magneto- and electroencephalography (M/EEG) recordings. Nevertheless, recent advances in M/EEG source reconstruction and clustering methods support complete phase-interaction mappings that are essential for uncovering the large-scale neuronal assemblies and their functional roles. These data show that synchroniza- tion is a robust and behaviorally significant phenomenon in task-relevant cortical networks and could hence bind distributed neuronal processing to coherent cognitive states.







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