Spatiotemporal Analysis of Multichannel EEG: CARTOOL

By Denis Brunet, Micah M. Murray, C. Michel. Computational Intelligence and Neuroscience, 2011

717 citations recorded by the source corpus.

This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/54dfd9c03e3d1b9b6b27bf029a5a60924ea074af

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In Cognitive Neuroscience

Citations in the corpus

Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI (2019, positive): “K-means clustering The free academic software Cartool was used for the microstate analysis (Brunet et al., 2011).”
The multisensory function of the human primary visual cortex. (2016, background): “…processing techniques, which improve not only the capability to localise multisensory interactions, but also to provide a more mechanistic characterisation of their neurophysiologic bases (reviewed in Murray et al. (2008), Brunet et al. (2011), Michel and Murray (2012) and Koenig et al. (2014)).”
Towards the utilization of EEG as a brain imaging tool (2012, methodology): “Cross-validation and other criteria were proposed to determine the optimal number of cluster maps (Brunet et al., 2011).”
Electroencephalographic Resting-State Networks: Source Localization of Microstates (2017, methodology): “We use Cartool’s adapted k-means clustering method to estimate the optimal set of topographies explaining the input EEG signal (Brunet et al., 2011; Murray et al., 2009; Pascual-Marqui et al., 1995).”
Microstates in Resting-State EEG: Current Status and Future Directions (2014, methodology): “Microstates are relatively easy to compute, and a few freely available software programs exist to help perform the analysis (Pascual-Marqui, 2002, Brunet et al., 2011, Koenig et al., 2011).”
Looming Signals Reveal Synergistic Principles of Multisensory Integration (2012, methodology): “Likewise, the use of an average reference receives support from biophysical laws as well as the implicit recentering of ERP data to such when performing source estimations (discussed by Brunet et al., 2011).”
EEG Source Imaging: A Practical Review of the Analysis Steps (2019, methodology): “Birot et al. (71) compared the LSMAC model with a BEM and a FEM model in a dataset of 38 epileptic patients in whom high-density scalp EEG, intracranial EEG and localization of the resection brain area that rendered the patient seizure-free was available.”