A study on MEG for algorithmic head shape extraction using statistical analysis

Man's tryst with mapping neural activity of the brain has been around since the last couple of decades. From MRI, fMRI to EEGs and then finally to MEG, it has been quite a long road. In our study we have used the last most rapidly advancing neural imaging technique (Magnetoencephalography) and have applied its result onto computational geometry for generating an anatomically and physiologically feasible head shape of the subject under consideration. We have used 340 channel MEG data set to generate a mesh based head shape. On the basis of this head shape, we have gone to access the recordings on the subject, simulate trigger delays, detect and remove artefacts such as spectral line contaminations using notch filters, averaging responses to estimating the source and finally, testing the result using statistical operations. The statistical operation that has been used is the t test and it gives a measure of the suitability of the head shape used for the particular subject which in turn throws light on the effectivity of the surface reconstruction algorithm used for this study. All the simulations have been done using the Brainstorm tool box of MATLAB software bundle.