[FieldTrip] ICA in TMS-EEG

[Herring, J.D. (Jim)]

Dear Bingshuo,



I agree with Max that it would be beneficial to see the timecourses as
well. Also, in case of ICA on TMS-EEG data I find it usefull to timelock
average the time courses of the ICA components to the onset of the TMS
pulse. That helps in identifying components that are related to the TMS
pulse.



Just from looking at the topographies I would say that component 7 is too
posterior for being a cranial muscle artifact given that you are
stimulating M1. Also, cranial muscle artifacts related to the TMS pulse
usually last up to about 15ms after stimulation onset (see for example:
http://fieldtrip.fcdonders.nl/_detail/development/tms/art_cranial_muscle.p
ng?id=tutorial%3Atms-eeg ) unless you are actually referring to a decay
artifact
(http://fieldtrip.fcdonders.nl/_detail/development/tms/art_decay.png?id=tu
torial%3Atms-eeg) , which can last up to one second. You said that you cut
and interpolated up to 18ms after stimulation so you should not see any
cranial muscle artifacts related to TMS.



Too me component 7 looks more like a topography related to posterior alpha
oscillations, however, to be sure we need to see the time courses.
Component 13 and 18 might be neck muscle artifacts, however, without
performing a timelock analysis of the ICA components and inspecting the
time courses it is hard to tell whether these are due to the TMS-pulse.



Component 42 and 54 could indeed just reflect two bad channels, at least
they seem to contain some variance that cannot be explained by a
combination of the other channels.



Best,



Jim



From: fieldtrip-bounces at science.ru.nl
[mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Bingshuo Li
Sent: dinsdag 26 november 2013 18:26
To: fieldtrip at science.ru.nl
Subject: [FieldTrip] ICA in TMS-EEG



Dear FieldTrip Community,

I recently started to analyze some TMS-EEG datasets and I encountered some
questions regarding to using ICA to remove eye movement/muscle artifacts
in our EEG data. As I am quite new to the analysis of TMS-EEG, I would
like to inquire the FT community for some hints or suggestions. Below are
the details of my questions:

//Description of Data Processing//

- EEG with 64 channel, sampling frequency 2500 Hz, electrode impedance
less than 5 kOhm

- Every epoch consists of 1s prior to and 1s after TMS (130-150 trials per
subject)

- TMS contaminated data points were cut out symmetrically -18ms to +18ms
relative to TMS onset. Cubic spline interpolation is used to fill in the
cut.

- Bandpass 0.5 - 80 Hz, with BUT and filter order 3.
- Discrete Fourier transform filter (cfg.dftfilter) to remove 50 Hz line
noise

- Visual inspection and rejection of trials with obvious unstable signal
or channels.

 //ICA//

- ICA algorithm: runica

- Demeaned data for ICA training (baseline is defined as the entire epoch
-1 to +1s)

- Unmixing matrix applied to non-demeaned data for component removal

/////QUESTIONS/////

Please see the image below for a typical result of ICA from a subject with
TMS applied at M1 (32 epochs for ICA training):
https://www.dropbox.com/s/chwo2jnwi72saba/ica1.png

Q1: It seems obvious to me that component 1 and 2 are of eyeblink origin.
However, what about component 5, 12, 20, 28? Topology-wise, they seem to
have a very anterior origin, but data in the time domain does not seem to
correlate with component 1 and 2 very well (judging visually..)

Q2: What can you say about components 7, 9, 13 and 18? Are these cranial
muscle artifacts?

Q3: Also, for components 42 and 54, given their high focality, are these
more or less a indication of bad/unstable electrodes?

- I guess maybe I am asking too many questions. I think my main problem
here is that I do not know what can be a good procedure / rules in
manually selecting ICA components for rejection? (I tried to look in the
literature but I couldn't find any that can answer my questions). And
sometime I have the feeling that my ICA results look like a mess and maybe
there were something wrong with my pre-processing or even data collection?


Thank you guys in advance for any input! I look forward to hearing from
you!

Regards,

Bingshuo Li

MSc. Student, Neuroprosthetic Group, CIN, Uni Tübingen

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