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Estimating correlation for a real-time measure of connectivity

There has recently been considerable interest in connectivity analysis of fMRI and scalp and intracranial EEG time-series. The computational requirements of the pair-wise correlation (PWC), the core time-series measure used to estimate connectivity, presents a challenge to the real-time estimation of the PWC between all pairs of multiple time-series. We describe a parallel algorithm for computing PWC in real-time for streaming data from multiple channels. The algorithm was implemented on the Intel Xeon™ and IBM Cell Broadband Engine™ platforms. We evaluated time to estimate correlation for signals recorded with different acquisition parameters as a comparison to real-time constraints. We demonstrate that the execution time of these efficient implementations meet real-time constraints in most instances.