Novel framework for small-world network connectivity analysis for MEG data

MEG is an emerging clinical tool to gather insight on neuronal dysfunction inside the human brain. This paper analyzes data saved as FIF file format at a small-world level. Traditionally, operating cost is very high for artifact removal. Also, the absence of a standardization with availability of more than a dozen MEG recording device vendors makes it difficult to store and process data followed by its comparison. This paper proposes a framework to avoid computation overheads, while it takes care of MEG data file format and calculates coherence with ease and efficiency. Small-world network is analyzed and 3D visualization is obtained.