Short-time windowed covariance: a metric for identifying non-stationary, event-related covariant cortical sites.
Short-time windowed covariance: a metric for identifying non-stationary, event-related covariant cortical sites.
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DOI：
10.1016/j.jneumeth.2013.10.005
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发表时间：
2014-01-30
影响因子：
3
通讯作者：
Rao, Rajesh P. N.
中科院分区：
文献类型：
--
作者：
Blakely, Timothy;Ojemann, Jeffrey G.;Rao, Rajesh P. N.
Electrocorticography (ECoG) signals can provide high spatio-temporal resolution and high signal to noise ratio recordings of local neural activity from the surface of the brain. Previous studies have shown that broad-band, spatially focal, high-frequency increases in ECoG signals are highly correlated with movement and other cognitive tasks and can be volitionally modulated. However, significant additional information may be present in inter-electrode interactions, but adding additional higher order inter-electrode interactions can be impractical from a computational aspect, if not impossible. In this paper we present a new method of calculating high frequency interactions between electrodes called Short-Time Windowed Covariance (STWC) that builds on mathematical techniques currently used in neural signal analysis, along with an implementation that accelerates the algorithm by orders of magnitude by leveraging commodity, off-the-shelf graphics processing unit (GPU) hardware. Using the hardware-accelerated implementation of STWC, we identify many types of event-related inter-electrode interactions from human ECoG recordings on global and local scales that have not been identified by previous methods. Unique temporal patterns are observed for digit flexion in both low- (10 mm spacing) and high-resolution (3 mm spacing) electrode arrays. Covariance is a commonly used metric for identifying correlated signals, but the standard covariance calculations do not allow for temporally varying covariance. In contrast STWC allows and identifies event-driven changes in covariance without identifying spurious noise correlations. Conclusions: STWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities.
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DOI：
10.1016/j.clinph.2009.04.024
发表时间：
2009-08
期刊：
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
影响因子：
--
作者：
Wilke C;van Drongelen W;Kohrman M;He B
通讯作者：
He B
影响因子：
4.4
作者：
Winterhalder, M;Schelter, B;Witte, H
通讯作者：
Witte, H
影响因子：
4.1
作者：
Blakely, Tim;Miller, Kai J.;Ojemann, Jeffrey G.
通讯作者：
Ojemann, Jeffrey G.
影响因子：
14.5
作者：
Sinai, A;Bowers, CW;Crone, NE
通讯作者：
Crone, NE
影响因子：
4.6
作者：
Schalk, G;McFarland, DJ;Wolpaw, JR
通讯作者：
Wolpaw, JR