<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class="">Hi Denis,<div class=""><br class=""></div><div class="">Thank you so much.&nbsp;</div><div class=""><br class=""></div><div class="">Yes I can compute time-frequency connectivity to get time resolved connectivity.&nbsp;</div><div class=""><br class=""></div><div class="">While I still have a more general question. When computing connectivity for band/time containing the evoked response, the result reflects true connectivity but also&nbsp;<span style="color: rgb(69, 69, 69);" class="">artifactual connectivity driven by&nbsp;</span><span style="color: rgb(69, 69, 69);" class="">evoke response of stimuli(eg: visual stimuli).&nbsp;</span><span style="color: rgb(69, 69, 69);" class="">For example, if there is time delay between the evoke response of two independent&nbsp;</span><span style="color: rgb(69, 69, 69);" class="">areas (A and B), the spurious connectivity between these A and B will be revealed. &nbsp;So in some studies, people remove evoked response from data before calculating the connectivity. But in examples in MNE-python, is there any consideration about this point?</span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><span style="color: rgb(69, 69, 69);" class="">Best,</span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><span style="color: rgb(69, 69, 69);" class="">Xiaoxu</span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><span style="color: rgb(69, 69, 69);" class=""><br class=""></span></div><div class=""><div><blockquote type="cite" class=""><div class="">On Apr 12, 2017, at 3:42 PM, Denis-Alexander Engemann &lt;<a href="mailto:denis.engemann@gmail.com" class="">denis.engemann@gmail.com</a>&gt; wrote:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class="">Hi Dave,<div class=""><br class=""></div><div class="">no all is computed across trials. When using a continuous Morlet Wavelet the cross-spectral density matrix is time-resolved but the same metrics can be computed. You always consume the trials dimensions in both case, irrespective of the connectivity metric.</div><div class=""><br class=""></div><div class="">Hope that helps,</div><div class="">Denis</div></div><br class=""><div class="gmail_quote"><div dir="ltr" class="">On Wed, Apr 12, 2017 at 4:15 PM David I Leitman &lt;<a href="mailto:leitman@mail.med.upenn.edu" class="">leitman@mail.med.upenn.edu</a>&gt; wrote:<br class=""></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word" class="gmail_msg"><div dir="auto" style="word-wrap:break-word" class="gmail_msg">Hi Dennis,<div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">&nbsp;I don’t mean to bud in, but a quick question</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">are both of these examples</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg"><a href="http://www.martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_connectivity_spectrum.html#sphx-glr-auto-examples-connectivity-plot-mne-inverse-connectivity-spectrum-py" class="gmail_msg" target="_blank">http://www.martinos.org/mne/stable/auto_examples/connectivity/plot_mne_inverse_connectivity_spectrum.html#sphx-glr-auto-examples-connectivity-plot-mne-inverse-connectivity-spectrum-py</a></div><div class="gmail_msg"><a href="http://www.martinos.org/mne/stable/auto_examples/connectivity/plot_cwt_sensor_connectivity.html#sphx-glr-auto-examples-connectivity-plot-cwt-sensor-connectivity-py" class="gmail_msg" target="_blank">http://www.martinos.org/mne/stable/auto_examples/connectivity/plot_cwt_sensor_connectivity.html#sphx-glr-auto-examples-connectivity-plot-cwt-sensor-connectivity-py</a></div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">calculating phase connectivity &nbsp;across trials that is- wPLI is calculated from the phase angle difference distributions at a particular point across trials</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">or is one or both often calculating &nbsp;phase angle differences from multiple points across the time from within each trial that is wPLI across time? &nbsp;</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">thanks //dave</div></div></div><div style="word-wrap:break-word" class="gmail_msg"><div dir="auto" style="word-wrap:break-word" class="gmail_msg"><div class="gmail_msg"><br class="gmail_msg"><blockquote type="cite" class="gmail_msg">

        
                
                
        
        
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                </div></blockquote><div class="gmail_msg"><blockquote type="cite" class="gmail_msg"><div class="gmail_msg">On Apr 12, 2017, at 9:08 PM, Denis-Alexander Engemann &lt;<a href="mailto:denis.engemann@gmail.com" class="gmail_msg" target="_blank">denis.engemann@gmail.com</a>&gt; wrote:</div><br class="gmail_msg m_-6061372045433441830Apple-interchange-newline"><div class="gmail_msg">Hi!<br class="gmail_msg">You can checkout the "cwt_morlet" option to get time resolved connectivity. See this example <a href="http://www.martinos.org/mne/stable/auto_examples/connectivity/plot_cwt_sensor_connectivity.html#sphx-glr-auto-examples-connectivity-plot-cwt-sensor-connectivity-py" class="gmail_msg" target="_blank">http://www.martinos.org/mne/stable/auto_examples/connectivity/plot_cwt_sensor_connectivity.html#sphx-glr-auto-examples-connectivity-plot-cwt-sensor-connectivity-py</a><br class="gmail_msg"><br class="gmail_msg">Hope that helps,<br class="gmail_msg">Denis<br class="gmail_msg"><div class="gmail_quote gmail_msg"><div dir="ltr" class="gmail_msg">On Wed, 12 Apr 2017 at 15:02, daisy &lt;<a href="mailto:smartcandies@163.com" class="gmail_msg" target="_blank">smartcandies@163.com</a>&gt; wrote:<br class="gmail_msg"></div><blockquote class="gmail_quote gmail_msg" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word" class="gmail_msg">Hi All,<div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">I have two questions when I compute source space connectivity.&nbsp;</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">(1)&nbsp;<span style="color:rgb(69,69,69)" class="gmail_msg">Is mne.connectivity.spectral_connectivity designed for stimulus-evoked data </span><span style="color:rgb(69,69,69);line-height:normal;font-family:'PingFang SC'" class="gmail_msg">?(Because I notice that the example data was stimulus-evoked.)</span></div><div class="gmail_msg"><span style="color:rgb(69,69,69);line-height:normal;font-family:'PingFang SC'" class="gmail_msg">(2) I want to know whether the connectivity was changed over time.&nbsp;</span><span style="color:rgb(69,69,69)" class="gmail_msg">Can I compute the connectivity under different time window (eg: 100-150ms, 150-200ms after stimulus onset). Is it reasonable?</span><span style="color:rgb(69,69,69)" class="gmail_msg">&nbsp;</span></div><div class="gmail_msg"><span style="color:rgb(69,69,69)" class="gmail_msg"><br class="gmail_msg"></span></div><div class="gmail_msg"><span style="color:rgb(69,69,69)" class="gmail_msg">Could anyone help me ?</span></div><div class="gmail_msg"><span style="color:rgb(69,69,69)" class="gmail_msg"><br class="gmail_msg"></span></div><div class="gmail_msg"><span style="color:rgb(69,69,69)" class="gmail_msg">Thank you so much.</span></div><div class="gmail_msg"><span style="color:rgb(69,69,69)" class="gmail_msg"><br class="gmail_msg"></span></div><div class="gmail_msg"><span style="color:rgb(69,69,69)" class="gmail_msg">-Best</span></div><div class="gmail_msg"><span style="color:rgb(69,69,69)" class="gmail_msg"><br class="gmail_msg"></span></div><div class="gmail_msg"><font color="#454545" class="gmail_msg">Xiaoxu</font></div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg"><br class="gmail_msg"><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg"><br class="gmail_msg"></div></div></div>_______________________________________________<br class="gmail_msg">
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