Key Laboratory of Biorheological Science and Technology, Ministry of Education,Couege of Bioengineering fi, Chongqing University, Chongqing 400004， P.R. China;Department ofElectrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, US 在期刊界中查找 在百度中查找 在本站中查找
Key Laboratory of Biorheological Science and Technology, Ministry of Education,Couege of Bioengineering fi, Chongqing University, Chongqing 400004， P.R. China;Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing 400 在期刊界中查找 在百度中查找 在本站中查找
It has been verified that the ICA can isolate sources from multichannel magnetoencephalography (MEG) signals. Based on the route of constrained ICA (cICA), this paper achieves a new solution of MEG inverse problem called functional source separation (FSS) by adding a functional constraint to the cost function of a basic ICA model. Source activity is obtained by applying this method to one MEG signal dataset under a selfpaced finger tapping task. The result is proved effective by calculating correlation coefficients between the weight vectors of function source separation method and the spatial filter coefficients of SAM method. It is found that finger tapping related functional source was localized in motor cortex of precentral gyrus. At the same time, the temporal and frequency information provided by FSS method could be a basis of exploring cortical control timing mechanisms associated with finger movements and extracting time frequency characteristics of the functional source.
