Arunkumar Akhil, Panday Ashish, Joshi Bharat, Ravindran Arun, Zaveri Hitten P
Electrical and Computer Engineering Department, University of North Carolina at Charlotte, NC 28223, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5190-3. doi: 10.1109/EMBC.2012.6347163.
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.
最近，功能磁共振成像（fMRI）以及头皮和颅内脑电图时间序列的连通性分析受到了广泛关注。用于估计连通性的核心时间序列测量方法——成对相关性（PWC）的计算需求，对多个时间序列的所有对之间的PWC实时估计提出了挑战。我们描述了一种并行算法，用于对来自多个通道的流数据实时计算PWC。该算法在英特尔至强™和IBM Cell宽带引擎™平台上实现。我们评估了估计不同采集参数记录的信号相关性所需的时间，以与实时约束进行比较。我们证明，这些高效实现的执行时间在大多数情况下都能满足实时约束。
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