<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; "><div><blockquote type="cite"><div dir="ltr"><div><p class="" style=""><font><b><span><span>1.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b><b>Should
I epoch the data before statistical analyses even though I am looking at the
differences between the Grand Average – within subjects?</b></font></p></div></div></blockquote><div>Yes, you need to extract data epochs to compute the grand average ERP. If you are computing the spectrum (based on the rest of your message), then epoch extraction is not necessary.</div><blockquote type="cite"><div dir="ltr"><div><p class="" style=""><font><b><span><span>2.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>I am HP filtering at 1Hz and using
Cleanline, automatic artifact rejection, visual artifact rejection.<span> </span>I have played with ICA component rejection,
but am not sure I have enough good data to do so. (I recorded pilot data on one
subject – I have 3 minutes of data for each condition and 14 electrodes).<span> </span><b>Is
there an optimal filtering method since I am using the EMOTIV?</b></font></p></div></div></blockquote><div>This is a very noisy system. To extract meaningful ICA components, the more filtering the better.</div><blockquote type="cite"><div dir="ltr"><div><font>
</font><p class="" style="margin-left:1in"><font><b><span><span>a.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>One of my committee said I should NOT filter
the data or remove artifacts since I am just looking for the Grand Average of
certain freq bands.<b><span> </span></b>Although I see his logic, I believe I should
still remove spurious data so that the mean is not skewed by muscle and other
artifacts… right?</font></p></div></div></blockquote><div>Yes, if you have strong drifts in your data or DC shifts, you will need to filter. Otherwise, it will ruin the whole spectrum.</div><blockquote type="cite"><div dir="ltr"><font>
</font><p class="" style=""><font><b><span><span>3.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>I was able to create a STUDY.<span> </span>I saved separate data sets with the
electrodes of interest. I was able to run power spectrum channel stats as a 3
(conditions) X 2(Left and Right) ANOVA.<span>
</span>I saw the t-tests between conditions and the interaction.<span> </span></font></p><font>
</font><p class="" style="margin-left:1in"><font><b><span><span>a.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>Is the power spectrum giving me the average
of the specified freq range?</font></p></div></blockquote>Yes<br><blockquote type="cite"><div dir="ltr"><div><font>
</font><p class="" style="margin-left:1in"><font><b><span><span>b.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>If I epoch the data, can I use Bootstrapping
instead of parametric or permutation?</font></p></div></div></blockquote><div><br></div>No, this is independent.<br><blockquote type="cite"><div dir="ltr"><div><font>
</font><p class="" style="margin-left:1in"><font><b><span><span>c.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>How can I get the actual values, including
the p values, of the stats?<span> </span>I see the
option to show a table with statistics containing the median, mean, mode, etc.,
but it rarely populates and is inconsistent. IS that statcond – on?</font></p></div></div></blockquote><div>Look at the history and add output to the function that plot the STUDY spectrum (the function name is std_spec). This function will return the statistics (which are computed by statcond).</div><br><blockquote type="cite"><div dir="ltr"><div><font>
</font><p class="" style="margin-left:1in"><font><b><span><span>d.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>If I show results from a bootsrap, what
statistical test was used to obtain the results? </font></p></div></div></blockquote><div>The t-value (paired or unpaired) or the F-value is computed. The bootstrap procedure randomly samples the data and allow EEGLAB to obtain confidence intervals for these values under the null hypothesis.</div><br><blockquote type="cite"><div dir="ltr"><div><font>
</font><p class="" style="margin-left:1in"><font><b><span><span>e.<span style="font-family:"Times New Roman";font-style:normal;font-variant:normal;font-weight:normal;line-height:normal;font-size-adjust:none;font-stretch:normal">
</span></span></span></b>Would I be better off using a ratio of Grand Average subtraction of the control from the FOCUS conditions. or pwelch in MATLAB -then dropping the data into SPSS for some non parametric analysis (WIlcoxon rank ordered)?</font></p></div></div></blockquote><div>Sure. You should try what you are comfortable with. The results should be similar. </div></div><br><style>
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