<meta http-equiv="Content-Type" content="text/html; charset=utf-8"><div dir="ltr"><div class="gmail_default" style="font-family:arial,helvetica,sans-serif">Dear Matti, </div><div class="gmail_default" style="font-family:arial,helvetica,sans-serif"><br></div><div class="gmail_default" style="font-family:arial,helvetica,sans-serif">Thank you for your response. Your answer has been very helpful.</div><div class="gmail_default" style="font-family:arial,helvetica,sans-serif"><br></div><div class="gmail_default" style="font-family:arial,helvetica,sans-serif">Best regards, </div><div class="gmail_default" style="font-family:arial,helvetica,sans-serif"><br></div><div class="gmail_default" style="font-family:arial,helvetica,sans-serif">Irene</div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">El jue., 30 may. 2019 a las 11:25, Matti Stenroos (<<a href="mailto:matti.stenroos@aalto.fi">matti.stenroos@aalto.fi</a>>) escribió:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Hello Irene,<br>
<br>
Dipole fitting is used, when one has prior knowledge that the data is <br>
supposed to arise from one (single-dipole) or a couple of (multi-dipole) <br>
focal source regions. In event-related analysis, one typically studies a <br>
phenomenon that is (assumed to be) time-locked to the stimulus and <br>
repeated (almost) identically stimulus-by-stimulus. Then, giving many <br>
stimuli and averaging the data, one can suppress other brain activity <br>
and bring up the studied phenomenon. If the phenomenon is supposed to <br>
arise from focal region(s), for example in the case of somatosensory <br>
evoked potentials, dipole fitting can perform excellently.<br>
<br>
If one has no such prior information / study setting, dipole fitting is <br>
not necessarily useful. In classical dipole fitting, one explains the <br>
measurement using the dipoles. A couple of dipoles often explain the <br>
data very well, but that does not mean that the result would make any <br>
sense. So, in resting-state data, one rather selects a method that a) <br>
explains the data using the whole brain space, or b) uses a spatial <br>
filtering approach that scans the source space without aiming to explain <br>
the measurement. Case a) would be minimum norm estimation and case b) <br>
for example beamforming.<br>
<br>
Cheers,<br>
Matti<br>
<br>
<br>
<br>
<br>
<br>
On 29/05/2019 17.18, Irene Varela Leniz wrote:<br>
> Dear Fieldtripers,<br>
> <br>
> I am following the tutorial "Dipole fitting of combined MEG/EEG data". <br>
> Here, there is a sentence /"information about how to fit dipole models <br>
> to the event-related fields (MEG) and potentials (EEG) of a single <br>
> subject". /Does the author want to express with this that the approach <br>
> dipole fitting is used with Event Related Potentials?<br>
> <br>
> I have been looking in literature and have only found studies based on <br>
> beamforming to study resting states. Does anyone know if the approach <br>
> dipole fitting can also be used in these cases? Or is it more convenient <br>
> to rely on beamforming approaches?<br>
> <br>
> Thank you for your attention.<br>
> <br>
> Best regards,<br>
> <br>
> Irene<br>
> <br>
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