Computational NeuroscienceShort-time windowed covariance: A metric for identifying non-stationary, event-related covariant cortical sites

- Short-time windowed covariance (STWC): a metric quantifying temporal relationships of cortical activity.
- STWC shows event-related increases in cortical signal covariance.
- Applied STWC to human electrocorticographic (ECoG) signals recorded from the cortical surface.
- Demonstrated temporal directionality in high frequency ECoG during finger flexion.
- A software implementation of STWC that uses consumer graphics processing units (GPUs).

BackgroundElectrocorticography (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.New methodIn 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.ResultsUsing 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.Comparison with existing methodsCovariance 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.ConclusionsSTWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities.