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Jun-ichiro Hirayama: “Unifying blind separation and coactivation clustering for resting-state EEG/MEG analysis”

 

September 11, 2015  Friday Lunch Seminar
12:15 〜 13:00

CiNet 1F Conference Room
“Unifying blind separation and coactivation clustering for resting-state EEG/MEG analysis”
Jun-ichiro Hirayama

ATR Neural Information Analysis Laboratories
http://www.cns.atr.jp/~hirayama/

Abstract:

Exploratory analysis of nonstationary functional brain connectivity during rest has recently received a lot of attention. Although electroencephalography (EEG) and magnetoencephalography (MEG) hold great promise due to their high temporal resolution, conventional two-stage analyses of EEG/MEG may be nonoptimal as they divide the problem into two stages making different prior assumptions about the data, i.e.

source separation (estimation) and subsequent connectivity pattern analysis. Recently we have proposed a new principled approach to unifying the two stages based on a hierarchical extension of independent component analysis, a standard "blind" source separation technique. In this talk, we present the basic idea of the method with some background on related develeopments in the field of unsupervised machine learning, and show the results of both simulation studies and an application to resting-state EEG data acquired in conjunction with a cued motor imagery/nonimagery task.

Reference: J. Hirayama, T. Ogawa and A. Hyvarinen. Unifying blind separation and clustering for resting-state EEG/MEG functional connectivity analysis. Neural Computation, 27(7), 1373-1404, 2015.

About CiNet's Friday Lunch Seminars:
The Friday Lunch Seminar is CiNet's main regular meeting series, held every week at 12:15 in the beautiful main lecture theatre on the ground floor at CiNet. The talks are typically 40mins long and orientated towards an inter-disciplinary audience. They are informal, social, and most people bring their own lunch to eat during the talk. They are open to anyone who is feeling curious and wants to come, regardless of where you work.