[Eeglablist] ICA misinformation

[Arnaud Delorme]

Hi Robert,

Thank you for sharing the manuscript.

Regarding the actual EEG data (figure 1), component activities seem quite noisy for blink components and the scalp topographies also appear patchy which is not typical of ICA blink or eye movement components which are usually quite smooth. It might be that the ICA decomposition was suboptimal (preprocessing of the data is not detailed and this might be the reason why - although sometimes ICA also fails to return meaningful components for reasons which are not well understood). The units on this figure are wrong by the way (ICA component activity or scalp topography are not in microvolt). So, the poor quality of this decomposition might be responsible for the phase distortion you are mentioning.

Regarding the simulated data, projection to scalp channels are usually done using a leadfield matrix that models the conductivity of the different brain layers. In this paper, it seems to be a simple linear combination (and the coefficients and the positions of the sources are not indicated which makes it hard to reproduce these results). The phase difference shown in Figure 5 (on the order of 0.001 radian for most of them) even if significant, do not appear convincing - is a change in phase of 0.001 radian really that critical. Changing the ICA stop threshold might actually dramatically affect these results.

I will take some time to download your dataset and have a look at it.
Best wishes,

Arno

> On Jun 13, 2017, at 11:34 AM, Robert Thatcher <rwthatcher2 at yahoo.com> wrote:
> 
> ICA is a wonderful tool for feature extraction but it has limitations when used to allegedly remove artifact.   The brain is not a large balloon filled with saline – there are hubs and clusters of neurons connected in networks (Brodmann areas, etc).  The phase differences between hubs are due to differences in the synaptic rise times and synaptic integration times and differences in conduction velocity, etc.   These differences are vital and critical to understanding brain function (see Nunez, “Electrical Fields of the Brain”, Oxford Univ. Press, 1981 or Walter Freeman and many others).
> 

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