<html>
<head>
<meta content="text/html; charset=ISO-8859-1"
http-equiv="Content-Type">
</head>
<body text="#000000" bgcolor="#FFFFFF">
<div class="moz-cite-prefix">Dear Ye,<br>
<br>
normalizing has different purposes. On the one hand, as Arjen
pointed out, it is necessary to normalize in source space to get
rid of the depth bias (well, alternatively you could normalize the
leadfields). On the other hand, it makes averaging over subjects
reasonable - otherwise differences in e.g. scalp conducitivity
(for EEG) or headshape and size (for MEG) might lead to biasing an
average in favour of some subjects. It is just the same reason as
using a baseline when analysing ERPs/ERFs.<br>
Furthermore, there is no correct way of normalizing. As I said,
you could also normalize the leadfield rather than normalizing by
conditions. I think the most important thing to remember is that<br>
a) you need some contrast or<br>
b) you need to normalize the leadfield<br>
<br>
For a) it would be sufficient to use Post#1/Post#2, but I would
rather not contrast two conditions normalized by two baselines.
This gets really hard in interpretation (e.g. is the observed
effect caused by a difference in baseline or a different in
stimulus processing?). My favourite is log(Post#1/Post#2) or
taking the z-score. Taking the logarithm has the advantage that
extreme values get squashed nearer together, thereby reducing the
influence of outliers. Z-scoring achieves a similar thing by
explicitly normalizing by the variance. <br>
I would suggest for you to create some synthetic signals or values
and play around with different ways of normalizing to get a
feeling for what you are doing and what influence this has. I test
different cases (e.g. 3: difference in prestim but no difference
in poststim, no difference in prestim but difference in poststim
and difference in prestim and poststim) and apply different
normalizations/contrasts.<br>
<br>
Good luck :)<br>
Best,<br>
Jörn<br>
<br>
<br>
On 7/3/2013 9:35 PM, Frank Mei wrote:<br>
</div>
<blockquote
cite="mid:CAF0J_Mv_=BFQhyF=g2-XyX6BvwSAV4uqjN2iG4B2rr__S7UT=Q@mail.gmail.com"
type="cite">
<div dir="ltr">
<div>Hello FieldtripList,</div>
<div><br>
</div>
<div>I am trying to differentiate brain areas responsible for
two different conditions using the method show in the
tutorial(<a moz-do-not-send="true"
href="http://fieldtrip.fcdonders.nl/tutorial/beamformer">http://fieldtrip.fcdonders.nl/tutorial/beamformer</a>),
and so far I have tried to subtract condition# 1 minus the #2,
and divided by the average of the baseline period (pre-stimuli
presentation), i.e., (Post#1-Post#2)./(Average of Pre#1 and
Pre#2). I think this division is for normalisation purposes.
Is this the right normalsation? What normalization do you
suggest to use? Is it necessary to normalise?</div>
<div><br>
</div>
<div>thanks</div>
<div>Ye </div>
</div>
<br>
<fieldset class="mimeAttachmentHeader"></fieldset>
<br>
<pre wrap="">_______________________________________________
fieldtrip mailing list
<a class="moz-txt-link-abbreviated" href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a>
<a class="moz-txt-link-freetext" href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a></pre>
</blockquote>
<br>
<br>
<pre class="moz-signature" cols="72">--
Jörn M. Horschig
PhD Student
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
Radboud University Nijmegen
Neuronal Oscillations Group
FieldTrip Development Team
P.O. Box 9101
NL-6500 HB Nijmegen
The Netherlands
Contact:
E-Mail: <a class="moz-txt-link-abbreviated" href="mailto:jm.horschig@donders.ru.nl">jm.horschig@donders.ru.nl</a>
Tel: +31-(0)24-36-68493
Web: <a class="moz-txt-link-freetext" href="http://www.ru.nl/donders">http://www.ru.nl/donders</a>
Visiting address:
Trigon, room 2.30
Kapittelweg 29
NL-6525 EN Nijmegen
The Netherlands</pre>
</body>
</html>