The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Spatiotemporal Analysis of Multichannel EEG: CARTOOL
Open accessAbstract
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.
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Cites
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Self-citationEEG source localization: Sensor density and head surface coverage
Song, J., Davey, C., Poulsen, C., Luu, P., Turovets, S., Anderson, E., Li, K., & Tucker, D. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.08.015 · Cited by 356
Electrophysiological source imaging: A noninvasive window to brain dynamics
He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297
EEG microstates are a candidate endophenotype for schizophrenia
da Cruz, J. R., Favrod, O., Roinishvili, M., Chkonia, E., Brand, A., Mohr, C., Figueiredo, P., & Herzog, M. H. (2020) · Nature Communications · doi:10.1038/s41467-020-16914-1 · Cited by 253
The multisensory function of the human primary visual cortex
Murray, M. M., Thelen, A., Thut, G., Romei, V., Martuzzi, R., & Matusz, P. J. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.08.011 · Cited by 219
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Metadata
- Authors
- Brunet, D., Murray, M. M., & Michel, C. M.
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2011
- Volume
- 2011
- Pages
- 1-15
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- OpenAlex
- W2089188198
- Cited by
- 731
- References
- 63
- FWCI
- 16.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
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