[Eeglablist] Individual participant contributions to source-space clusters

[Matt Gerhold]

ICA gurus:

I would be very grateful if you can assist me in understanding some of the
methods and data structures from your toolbox. Specifically, I would like
to gain some clarity regarding working with clusters of independent
components in the source-space, wherein individual participants contribute
source-localised components to a group level cluster.



*What I have done and what I have observed*

I have pre-processed the data (including applying ICA), performed dipole
source localisation, and clustered the components. The resultant group
level clusters make sense: I am using a task that requires that
participants respond with a button press, or alternatively withhold a
button press when cued to do so. I have observed upper alpha (*rolandic mu*)
desynchronization in the component cluster localised to the motor region
contralateral to the button-pressing finger. In addition, other such
event-related spectral features suggest that the clustering and dipole
localisation have been successful.

When I inspect the clustered components, I have observed that some clusters
may contain two components from the same participant—the two components
have a similar scalp distribution and spectra leading to similar
clustering. In certain instances, some group level clusters may not have
contributions from one or two participants within the group. Therefore, on
the group level the time-frequency data looks good; however, the observed
group level dynamics do not easily translate into individual participant
measures that can be tabulated for a more rigorous statistical analysis.



*What I am trying to achieve*

I need to check for confounding (a covariate analysis) and possible
mediating effects using a general linear model framework. For this, I have
a collection of measurements for each participant and require a spectral
feature representative of the group level cluster for each participant in
order to perform my analysis. For example, alpha power over the
contralateral motor cortex in a post-stimulus onset window would be a
spectral feature; I would potentially use age-at-recording as a covariate
in my analysis to see if maturational effects explain variation in spectral
power over-and-above the experimental manipulation(s).



*Questions*

Is ICA appropriate for what I want to achieve, or do you suggest other
methods of source localisation?

Do you have a strategy on how to approach the EEGLAB data-structures on a
participant level to access the information I require?



Any advice related to the above questions would be greatly appreciated.
Many thanks in advance.

Kind Regards,

Matthew
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20160920/2a965fb6/attachment.html>