<div dir="ltr"><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">
Dear Roey,<br></blockquote><div> </div><div> I don't have answer to your question but I have some thoughts. </div><div> </div><div style="text-align:justify"> Firstly , you can cut your EEG resting state data into trials (2s per trial) . Since the dominant frequency of resting state is alpha , you can build frequency domain beamformer 'DICS' with <span style="color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif;font-size:15.454545021057129px;line-height:20.454544067382813px;text-align:justify">(cfg.normalize='yes') to </span><font color="#000000" face="Calibri, Geneva, Arial, Verdana, sans-serif"><span style="font-size:15.454545021057129px;line-height:20.454544067382813px">normalize the lead field , which will avoid bias toward the center of the head (<a href="http://fieldtrip.fcdonders.nl/tutorial/beamformer">http://fieldtrip.fcdonders.nl/tutorial/beamformer</a></span></font><span style="font-size:15.454545021057129px;line-height:20.454544067382813px;color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif">). After that , you can use virtual channel (all voxels) to extract the time series (</span><font color="#000000" face="Calibri, Geneva, Arial, Verdana, sans-serif"><span style="font-size:15.454545021057129px;line-height:20.454544067382813px"><a href="http://fieldtrip.fcdonders.nl/tutorial/shared/virtual_sensors">http://fieldtrip.fcdonders.nl/tutorial/shared/virtual_sensors</a></span></font><span style="color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif;font-size:15.454545021057129px;line-height:20.454544067382813px">). However, I am not sure whether it makes sense or not . Maybe the experts can comment on it.</span></div>
<div style="text-align:justify"><span style="color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif;font-size:15.454545021057129px;line-height:20.454544067382813px"><br></span></div><div style="text-align:justify">
<span style="color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif;font-size:15.454545021057129px;line-height:20.454544067382813px"> Best,</span></div>
<div style="text-align:justify"><span style="color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif;font-size:15.454545021057129px;line-height:20.454544067382813px"> Haiteng</span></div>
<div style="text-align:justify"><span style="color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif;font-size:15.454545021057129px;line-height:20.454544067382813px"> </span></div><div style="text-align:justify">
<span style="color:rgb(0,0,0);font-family:Calibri,Geneva,Arial,Verdana,sans-serif;font-size:15.454545021057129px;line-height:20.454544067382813px"> </span></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">
<br><br>
Message: 2<br>
Date: Tue, 15 Apr 2014 17:03:00 +0300<br>
From: Roey Schurr <<a href="mailto:roeysc@gmail.com">roeysc@gmail.com</a>><br>
To: FieldTrip discussion list <<a href="mailto:fieldtrip@science.ru.nl">fieldtrip@science.ru.nl</a>><br>
Subject: [FieldTrip] Resting State Data Source Reconstruction -<br>
covriance matrix in beamformer, and "avg" field in eLORETA<br>
Message-ID:<br>
<CAHm4wZA4KuZeG7O0V0xL2G6jM4Cb9usY-Cj7_Fh9c=<a href="mailto:zYfDVYjg@mail.gmail.com">zYfDVYjg@mail.gmail.com</a>><br>
Content-Type: text/plain; charset="utf-8"<br>
<br>
Dear Fieldtrippers,<br>
<br>
<br>
First of all, a big thank you to all members of the community ? it is a<br>
wonderful place to learn in and share ideas, thoughts and solutions to<br>
common problems we all face.<br>
<br>
<br>
I am writing you since I fear I might be missing something trivial: I am<br>
trying to compute an inverse solution to to resting-state EEG data. Two<br>
question arise, depending on the method I am trying to use:<br>
<br>
<br>
1) Using a beamformer technique such as LCMV, a covariance matrix is<br>
needed. However, since the data is not time-locked (like ERP), the<br>
covariance matrix cannot be easily computed over the averaged trials. Can<br>
it be estimated using different resting-state trials?<br>
<br>
2) Using eLORETA as the source reconstruction method (since my goal is<br>
a time-domain source reconstruction), it seems the the "avg" field is neeed<br>
in the data. Once again, no such average is available, since these are not<br>
ERP trials.<br>
<br>
Would you advise me to revise the code and try to fit it to non-ERP data,<br>
or is there some easier way I'm missing?<br>
<br>
<br>
Thank you all,<br>
<br>
Best,<br>
<br>
roey<br><br><br></blockquote></div><br clear="all"><div><br></div>-- <br><div><div><font face="arial, helvetica, sans-serif">Haiteng Jiang</font></div><div><font face="arial, helvetica, sans-serif">PhD candidate</font></div>
<div><font face="arial, helvetica, sans-serif">Neuronal Oscillations Group</font></div><div><font face="arial, helvetica, sans-serif">Donders Institute for Brain, Cognition and Behaviour</font></div><div><font face="arial, helvetica, sans-serif">Centre for Cognitive Neuroimaging</font></div>
<div><font face="arial, helvetica, sans-serif">Radboud University Nijmegen</font></div><div><span style="line-height:18px;background-color:rgb(255,255,255)"><font face="arial, helvetica, sans-serif"><br></font></span></div>
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