[Eeglablist] Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG “leakage correction”

pascualmarqui at gmail.com pascualmarqui at gmail.com
Mon Aug 28 16:48:19 PDT 2017


Dear Colleagues,
The pre-print at:
https://doi.org/10.1101/178657
might be of interest to those working in the field of brain
connectivity based on signals of electric neuronal activity. It is
shown that "leakage correction" in the form of "signal
orthogonalization" consistently produces false connectomes. More
importantly, a new method is proposed for the resolution ("unmixing")
of electrophysiological signals, based on "innovations
orthogonalization".

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Abstract : The problem of interest here is the study of brain
functional and effective connectivity based on non-invasive EEG-MEG
inverse solution time series. These signals generally have low spatial
resolution, such that an estimated signal at any one site is an
instantaneous linear mixture of the true, actual, unobserved signals
across all cortical sites. False connectivity can result from analysis
of these low-resolution signals. Recent efforts toward “unmixing” have
been developed, under the name of “leakage correction”. One recent
noteworthy approach is that by Colclough et al (2015 NeuroImage,
117:439-448), which forces the inverse solution signals to have zero
cross-correlation at lag zero. One goal is to show that Colclough’s
method produces false human connectomes under very broad conditions.
The second major goal is to develop a new solution, that appropriately
“unmixes” the inverse solution signals, based on innovations
orthogonalization. The new method first fits a multivariate
autoregression to the inverse solution signals, giving the mixed
innovations. Second, the mixed innovations are orthogonalized. Third,
the mixed and orthogonalized innovations allow the estimation of the
“unmixing” matrix, which is then finally used to “unmix” the inverse
solution signals. It is shown that under very broad conditions, the
new method produces proper human connectomes, even when the signals
are not generated by an autoregressive model.
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Cordially,
Roberto
...
Roberto D. Pascual-Marqui, PhD, PD
The KEY Institute for Brain-Mind Research, University of Zurich
Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka
[www.keyinst.uzh.ch/loreta] [scholar.google.com/citations?user=pascualmarqui]


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