| Autor: |
Arunkumar, Akhil, Panday, Ashish, Joshi, Bharat, Ravindran, Arun, Zaveri, Hitten P. |
| Zdroj: |
2012 Annual International Conference of the IEEE Engineering in Medicine & Biology Society; 1/ 1/2012, p5190-5193, 4p |
| Abstrakt: |
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. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
