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<p class="MsoPlainText">Dear list,<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText">I wish to share this recent result that I believe may help avoiding pitfalls in EEG preprocessing pipelines.<o:p></o:p></p>
<p class="MsoPlainText"><span style="color:black">In short, we discovered that <b>
PCA dimension reduction of EEG will likely decrease the quality of your subsequent ICA decomposition</b>.<o:p></o:p></span></p>
<p class="MsoPlainText"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoPlainText"><span style="color:black">You can find more details in the paper
<o:p></o:p></span></p>
<p class="MsoPlainText"><i><span style="color:black">"Artoni, Fiorenzo, Arnaud Delorme, and Scott Makeig. "Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition." NeuroImage
175 (2018): 176-187."<o:p></o:p></span></i></p>
<p class="MsoPlainText"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoPlainText"><span style="color:black">Elsevier makes it possible to download the paper for free (regardless of subscription to NeuroImage) until May 29 at this link:<o:p></o:p></span></p>
<p class="MsoPlainText"><span style="font-family:"Arial",sans-serif"><a href="https://authors.elsevier.com/a/1WsOL3lc~r3Ek-" target="_blank"><span style="color:#007398">https://authors.elsevier.com/a/1WsOL3lc~r3Ek-</span></a><o:p></o:p></span></p>
<p class="MsoPlainText"><span style="color:black"><o:p> </o:p></span></p>
<p class="MsoPlainText"><span style="color:black">Best,<o:p></o:p></span></p>
<p class="MsoPlainText"><span style="color:black">Fiorenzo<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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