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Stanley Klein, David Kim, Thom Carney; EEG and MEG Time Functions Are the Same. Journal of Vision 2010;10(7):926. doi: 10.1167/10.7.926.
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© ARVO (1962-2015); The Authors (2016-present)
Previous studies with simultaneous EEG and MEG recordings, have reported significant intermodal differences. These differences reflect the differential contributions from multiple sources due to the different dependencies of EEG and MEG on tissue conductivity. Our multifocal stimuli composed of 32 small patches that randomly check-reversed at 30Hz activate in a time-locked fashion only early visual areas, while effectively suppressing higher level processes. We suspect it is this selective activation and the availability of many patches for SVD analysis that permits the impressive intermodal agreements in our study.
We performed three analyses to assess the time-function similarity.
1) Estimation of the overall fit by correlation measures. EEG/MEG correlation coefficients for the first three SVD time-function components were [0.99, 0.99, 0.99], [0.94, 0.97, 0.97] and [0.61, 0.57, 0.88] ([S1, S2, S3] denotes the three subjects).
2) ChiSquare estimation of intermodal time-lag. To p=.001 confidence, we can detect the difference between EEG and MEG when the EEG signal is shifted by [1.6, 1.9, 1.6], [1.5, 1.3, 1.1] and [3.7, 9.3, 1.5] milliseconds for the three components. These surprisingly small values are a powerful demonstration of EEG/MEG agreement especially since we allowed arbitrary linear combination of the EEG components to match each MEG component.
3) Identification of signal differences at specific temporal locations. In order to carefully test the significance of temporal regions where signals differ, we used cluster-based permutation analysis to determine the location and significance of these differences. We found that for the three subjects, the cluster analysis (p<0.05) resulted in only 3, 2, and 2 clusters that occupied 4, 2.4, and 2.9 percent of the total signal durations, well within the significance criterion.
This close time function agreement is a powerful step enabling EEG and MEG to complement each other for solving the source localization inverse problem.
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