<div dir="ltr"><div class="gmail_default" style="color:rgb(51,51,153)"><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"><div dir="ltr"><div style="display:inline">Hi Joseph, some quick responses below, good luck!</div><div><div style="display:inline"><br></div></div><div><div style="display:inline"><br></div></div><div><div style="display:inline"><br></div></div><div><div style="display:inline"><br></div></div><div><div style="display:inline"><br></div></div><div><div style="display:inline">*************RESPONSE FOR JOSEPH************************<wbr>******************************<wbr>**</div></div><div><div style="display:inline">first </div>see the following on google scholar<div style="display:inline"> which provides plenty of general guidelines related to your question.</div><br><br>Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400</div><div><br><div><div>Be sure to also review about ~20 recent publications from respected authors using P3 measures in high-impact journals for general guidelines.</div><div>You may also want to examine reviews about different subcomponents of the P3, such as P3a, P3b, etc... all easy to find on google scholar</div><div><br></div><div><br></div><div>THEN....</div><div><br></div><div>1a. It depends on what your ERP looks like. You should review major methods for computing ERP metrics from various handbooks for ERP/EEG such as the ones from Luck or Handy. See also online tutorials from Luck for ERPLAB that cover various ERP metrics. See also methods in the research articles that you are trying to emulate. You should be able to find at least 20 or 30 articles on Google Scholar, each of which will describe how they computed their P3 ERP metrics.</div><div><br></div><div>1b. Use time windows based on published research that has used similar paradigms as yours. </div><div>Also pick time windows based on what you see in your grand-average ERPs and your single-subject ERPs (the latter will be more variable of course).</div><div><br></div><div><br></div><div>1c. Yes usually/often midline at frontal, central and central-posterior sites (but depends on a variety of factors).</div><div>Refer to reviews (such as from Polich or others) on the P3. I think it depends on the particular protocol, type of stimuli, type of population.</div><div>Reviewing 10 to 20 recent articles (or classic articles) will give you a good enough idea of where to expect P300 dynamics for your paradigm.</div><div>Anyway, if you just type "p300 EEG topomap" into google images, you can get a good idea of the scalp distribution of p300.</div><div style="color:rgb(34,34,34)"><br></div><div style="color:rgb(34,34,34)"><br></div><div>1d. Get the the time series from the data for a particular channel or group of channels. Determine what part of that time series is your time window of interest. Then average up that data within the time window. You would do so by isolating the timewindow for that channel(s) into a matrix of data, and then taking the average via a matlab function. Of course, please familiarize yourself (Point 1a above) with the various ways to compute ERP metrics.Note also your data should likely be appropriately baselined, etc...</div><div><br></div><div>2. See Luck's and ERPLAB's pages on that measure. </div><div>Just google "half area latency eeglab" and there are several useful links and examples.</div><div>The blog page that comes up as one of the first links, by<span style="color:rgb(0,0,0);font-family:'Open Sans';font-size:14.4px;line-height:18.72px"> Lindeløv, seems kind of useful for your case, which should be translatable to study metrics.</span></div><div>Also google "eeglablist and your topic" for some other past eeglablist discussion related to your topic. </div></div></div></div></blockquote><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div> </div></div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Sep 25, 2017 at 6:53 AM, Joseph Nuamah <span dir="ltr"><<a href="mailto:jknuamah@aggies.ncat.edu" target="_blank">jknuamah@aggies.ncat.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div>Dear All,</div><div><br></div><div>In my STUDY design, there are two conditions. I want to, among other things, determine whether the difference between P300 amplitude and latency for both conditions is statistically significant.</div><div><br></div><div>Please find my questions below:</div><div>1a. Given that P300 is a broad component, how do I derive temporal windows for analysis ?<br>1b. In particular, what time after stimulus onset should that be applied to? </div><div>1c. Will that vary across channels (I intend to use midline sites Fz, Cz, and Pz)?<br>1d. How do I average amplitudes within these temporal windows for each participant ?</div><div><br></div><div>For say channel PZ, I am able to retrieve erpdata and erptimes from </div><div>[STUDY erpdata erptimes] = std_erpplot(STUDY,ALLEEG,'<wbr>channels',{ 'PZ'});</div><div>I read about fractional-area (50%) latency in the literature. </div><div><br></div><div>2. Can I determine half-area latency with corresponding amplitude for P300 in STUDY?</div><div><br></div><div>Kindly help.</div><div><br></div><div>Thanks!</div><span class="HOEnZb"><font color="#888888"><div><br></div><div>Joseph</div></font></span></div>
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