A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency wi...Expand abstract
- Publication status:
- Publisher copy:
- Copyright date:
Inferring task-related networks using independent component analysis in magnetoencephalography.
Views and Downloads
If you are the owner of this record, you can report an update to it here: Report update to this record