[Eeglablist] ICA and dipfit: high residual variance

[Giovanni Vecchiato]

Dear all,

 

I'm performing an ICA followed by a dipole source localization (DIPFIT) and
I'm not satisfied by the outcome of the analysis because it returns too high
values of residual variance (RV%).

 

Here the details of the EEG signal acquisition and processing (eeglab
v13.6.5b):

 

1.    Data acquisition with EGI (srate = 500 Hz; 129 channel): 

a.    two blocks of an execution task (EX) + two blocks of motor imagery
task (IM) (overall, around 17 minutes of recording)

 

2.    Signal processing

a.    PREP pipeline with line frequency removed = [50 100 150 200]; the
other parameters are the default ones

b.    high pass filtering @ 0.1 Hz

c.    data segmentation in EX and IM conditions ([-1, 6] seconds), 40 trials
per condition

d.    PCA on the IM dataset (35 trials remained after trial rejection)

e.    Extended ICA with 27 PCs to retain (which explain the 99% of the
variance)

f.     DIPFIT analysis using the MNI template and manual co-registration
(the actual EGI channel positions are correctly loaded) returning an average
residual variance of (0.5 +/- 0.2) which values seem too high to be taken
into account for a following analysis of ICs.

 

I have tried several modification to the above processing chain (e.g. low
pass filtering @ 45 Hz; ICA on a larger number of PCs and on a larger number
of trials (80); ICA performed with AMICA; ICA performed with Brain Vision
Analyzer - Extended ICA) but with no improvement of the results. I have also
tried on different datasets and tasks with the same results.

 

Do you all have any suggestion to achieve lower DIPFIT residual variance
which are plausible from the neurophysiological point of view?

I can also share a sample dataset if you consider it useful.

 

Thanks in advance,

Best,

Giovanni 

 

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