<div dir="ltr"><div class="gmail_default" style="color:#333399">Hello Davida, you probably want to just refer to the nearest grey matter. If you are working with multiple subjects, you should get a cloud of dipoles (using study) that are near some grey matter. </div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">There is a lot of error that is integrated into the final dipole solution you are looking at, don't expect it to be perfect or spot on.</div><div class="gmail_default" style="color:#333399">There is also plenty of error (usually) in source estimates. They are not perfect solutions.</div><div class="gmail_default" style="color:#333399">Thus overall, even a 10 MM (or more) could be expected, especially if you have low channel count and are using an average head that is not subject-specific.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">Note that the validity of dipole localizations is dependent in part on </div><div class="gmail_default" style="color:#333399">A. whether you have an individualized single-subject head model </div><div class="gmail_default" style="color:#333399">B. whether you have integrated single-subject 3D head locations</div><div class="gmail_default" style="color:#333399">C. which specific head model you are using (3-shell, 4-shell, etc..)</div><div class="gmail_default" style="color:#333399">D. whether your channels are closely/correctly registered to the head</div><div class="gmail_default" style="color:#333399">E. how many channels you have (64+ is better, 128+ is best).</div><div class="gmail_default" style="color:#333399">F. I believe that dipole localizations are often usually more valid for ERP than resting data.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">You may also want to review chapters on dipole fitting and source estimation. You amy also want to review other software that uses source estimation (e.g., Brainstorm, MNE python).</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">You may also want to check out Ojeda's distributed source estimator for eeglab that uses openMEEG. You can google that on eeglablist for recent version.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Nov 12, 2017 at 12:14 PM, Davida Streett <span dir="ltr"><<a href="mailto:davida_s2000@yahoo.com" target="_blank">davida_s2000@yahoo.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div style="color:#000;background-color:#fff;font-family:Courier New,courier,monaco,monospace,sans-serif;font-size:16px"><div id="m_-5978924877123900162yui_3_16_0_ym19_1_1510516117765_10108">EEGLAB community especially dipole fitting gurus,</div><div id="m_-5978924877123900162yui_3_16_0_ym19_1_1510516117765_10098" dir="ltr"> AMICA applied to my 128 channel data produced a component that certainly seems to correspond to a neural dipole, with a pronounced alpha peak, low RV%, passes all automated tests, convincing topography etc. (There is no left-right symmetry so I believe it should be fit by one not two dipoles.) And several connectivity/causality measures in SIFT suggest that this component/dipole is absolutely integral to what I am studying in the brain. But dipfit localizes it to white matter (and its Tailarach coordinates -5 -12 32 are not within 5 mm of gray matter). I don't have MRI or Polhemus data, but would still like to take a stab at determining its most likely true location. Any help would be very appreciated. </div><div id="m_-5978924877123900162yui_3_16_0_ym19_1_1510516117765_10281" dir="ltr"> Thanks,</div><div id="m_-5978924877123900162yui_3_16_0_ym19_1_1510516117765_10320" dir="ltr">Davida </div></div></div><br>______________________________<wbr>_________________<br>
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