Neuroinformatics

, Volume 13, Issue 2, pp 167–174 | Cite as

NeuralAct: A Tool to Visualize Electrocortical (ECoG) Activity on a Three-Dimensional Model of the Cortex

Software Original Article

Abstract

Electrocorticography (ECoG) records neural signals directly from the surface of the cortex. Due to its high temporal and favorable spatial resolution, ECoG has emerged as a valuable new tool in acquiring cortical activity in cognitive and systems neuroscience. Many studies using ECoG visualized topographies of cortical activity or statistical tests on a three-dimensional model of the cortex, but a dedicated tool for this function has not yet been described. In this paper, we describe the NeuralAct package that serves this purpose. This package takes as input the 3D coordinates of the recording sensors, a cortical model in the same coordinate system (e.g., Talairach), and the activation data to be visualized at each sensor. It then aligns the sensor coordinates with the cortical model, convolves the activation data with a spatial kernel, and renders the resulting activations in color on the cortical model. The NeuralAct package can plot cortical activations of an individual subject as well as activations averaged over subjects. It is capable to render single images as well as sequences of images. The software runs under Matlab and is stable and robust. We here provide the tool and describe its visualization capabilities and procedures. The provided package contains thoroughly documented code and includes a simple demo that guides the researcher through the functionality of the tool.

Keywords

Brain Imaging ECoG EEG MEG DOT Matlab 

Notes

Acknowledgments

This work was supported in part by grants from the US Army Research Office [W911NF-08-1-0216 and W911NF-07-1-0415] and the NIH [EB006356 and EB000856].

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Anatomy & NeurobiologyWashington University in St. LouisSt. LouisUSA
  2. 2.National Resource Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of HealthAlbanyUSA

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