Many have heard experts in the neurofeeback field state vehemently that the “ICA deartifacting ruins the EEG”, and that “remontaging to a Laplacian montage ruins coherence”. There are internet tutorials attempting to support these opinions. This self-publication on-line on a commercial site is not the same as peer review, and many publish bad opinions without an alternative approach even considered.
Rather than engage in the meaningless back and forth of mere opinions, I thought it was better to wait for the decision of the jury.. a jury of our peers inherent to the peer reviews in professional publications seen in the field of neuroscience. I comfortably accept the judgment of the field’s journal’s editors.
Harvard’s famous Electroencephalographer, Frank Duffy M.D. just published a large scale well designed study. Some of the key aspects in the paper are highlighted and discussed below:
“Remaining eye blink and eye movement artifacts, which may be surprisingly prominent even during the eyes closed state, were removed by utilizing the source component technique [42, 43] as implemented in the BESA (BESA GmbH, Freihamer Strasse 18, 82116 GrÃ¤felfing Germany) software package”
Also see Duffy’s statement:
“… artifacts cannot be removed from an entire EEG data set alone by visual inspection and direct elimination of electrodes and/or frequencies where a particular artifact is most easily apparent. An established approach to reduce further any persisting artifact contamination of processed coherence data involves multivariate regression.”
This summarized material refutes the often stated criticism of ICA deartifacting we hear in the field so commonly, even though it is totally incorrect.
Another feature in the study is the montage used for coherence.. A Laplacian technique of current source density (CSD) is used by Duffy, as seen in this quote:
“The CSD technique was employed as it provides reference independent data that are primarily sensitive to underlying cortex and relatively insensitive to deep/remote EEG sources. Srinvasan et al.  point out that EEG coherence is often used to assess functional connectivity in human cortex. However, moderate to large EEG coherence can also arise simply by the volume conduction of current through the tissues of the head (and)EEG coherence appears to result from a mixture of volume conduction effects and genuine source coherence. Surface Laplacian EEG methods minimize the effect of volume conduction…”
Obviously the use of laplacian montage did not ruin the coherence, as is commonly stated in our field. The coherence is clarified with this Laplacian montage selection, not ruined.
The neuroscience publications’ peer review process has accepted these techniques, both the ICA deartifacting and the use of Laplacian montages for calculation of coherence.
The paper is approved for copying and distribution, so feel free to share it with those who doubt these approaches are accepted at all, based on the common bad-mouthing these approaches have received within our field.
Frank H Duffy and Heidelise Als
BMC Medicine 2012, 10:64 doi:10.1186/1741-7015-10-64
Published: 26 June 2012
The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. EEG coherence studies confirm connectivity changes. However, genetic-, MRI-, and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples, and for EEG, lack of attention to group-specific artifact.
Of the 1304 subjects with ages ranging from 1 to 18 years old and assessed with comparable EEG studies who participated in this study, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2-12 year old subsample consisted of 430 ASD- and 554 C-group subjects (n=984). Discriminant function analysis (DFA) determined the spectral coherence factors’ discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.
Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2-12 year olds the 40 factors showed highly significant group differences (p<0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2-4 year olds (C, 90.6%; ASD, 98.1%); 4-6 year olds (C, 90.9%; ASD 99.1%); and 6-12 year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced as well as increased long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).
Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance- coherences may indicate poor local network function. The increased long-distance- coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.