Magnetoencephalography (MEG) is a non-invasive procedure similar to electroencephalography (EEG) in terms of basic principles and analysis. Subjects sit with their head inside a helmet-shaped device which contains magnetic field sensors. These superconducting quantum interference device (SQUID) sensors passively detect the weak magnetic fields (around 10 -14 Tesla) produced by brain activity. The NIMH MEG system has 275 sensors over the head and additional reference coils for background noise elimination. Functional neuroimaging studies record the MEG while subjects perform various cognitive or manual tasks. The goal is to determine the source distribution of brain activity related to the underlying neurophysiology of cognitive and motor behavior. Under favorable conditions, spatial localization of current sources with whole head MEG is on the order of 2-3 mm at a temporal resolution better than 1 ms.
Essential to MEG studies is the complex nature of the signal processing and visualization required to analyze the recorded data. Projects in several related areas are available depending on the background and interests of interns. These include:
- Refinement of spatio-temporal processing using multi-taper spectrum and wavelet techniques
- Mapping of MEG data onto cortical surface maps from segmented MRI data
- Distributed source methods
- Prediction of movement from motor area activity.
We utilize several existing tool packages, some are MATLAB based, but all run under Linux.