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.
Electrical and Computer Engineering Department, University of North Carolina at Charlotte, NC 28223, USA. aarunkum@uncc.edu
Akhil
A
Arunkumar
Ashish
A
Panday
Bharat
B
Joshi
Arun
A
Ravindran
Hitten P
HP
Zaveri
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003199:Computer Systems; D063132:Connectome / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic