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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Dear Hui,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Your data was probably not full rank before you high-pass filtered. You can check the condition of the data by examining the eigenvalues:<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'> >> eig( EEG.data(2:63,:) * EEG.data(2:63,:)’;<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>If the smallest eigenvalues are negative or much closer to zero than the largest eigenvalue, then the data is not well conditioned, and PCA is used to reduce the dimensionality. Data that is not full rank is like having 3 dimensional data that all lies on a plane.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>If there are large drifts in the data relative to the EEG signal variance, or epochs with different means, that might explain why the raw data is not full rank but the high-pass filtered data is.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>It is important to high-pass filter your data before running ICA if there is any non-stationary “drift” etc. in the data. ICA assumes stationariity of the data, and in particular a constant channel mean (no drift). High-pass filtering should be done on the continuous data (before epoching).<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Best,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Jason<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> eeglablist-bounces@sccn.ucsd.edu [mailto:eeglablist-bounces@sccn.ucsd.edu] <b>On Behalf Of </b>hui zhang<br><b>Sent:</b> Wednesday, March 09, 2011 8:46 AM<br><b>To:</b> David Post<br><b>Cc:</b> eeglablist<br><b>Subject:</b> Re: [Eeglablist] The number of the ICA components is smaller than the number of channels.<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal style='margin-bottom:12.0pt'>Hi David,<br><br>Thank you. But my data does have 66 channels, and I am interested in 2:63. And for different data sets, the output of detected channel number is different.<br><br>After high pass filter, it became normal. Anyone here can explain this?<o:p></o:p></p><div><p class=MsoNormal>On Wed, Mar 9, 2011 at 6:55 AM, David Post <<a href="mailto:djp227@cornell.edu">djp227@cornell.edu</a>> wrote:<o:p></o:p></p><p class=MsoNormal>Hello Hui,<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>I believe the problem you are experiencing is due to the fact that your data has 39 channels, and in your code you use the option 'chanind' and set the channels to 2:63. You might fix your problem by instead using the command:<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><blockquote style='margin-top:5.0pt;margin-bottom:5.0pt'><p class=MsoNormal><i>EEG = pop_runica(EEG, 'icatype', 'runica', 'chanind', 1:39, 'extended', 1);</i><o:p></o:p></p></blockquote><p class=MsoNormal>Regards,<o:p></o:p></p></div><div><p class=MsoNormal>David<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p><div><div><p class=MsoNormal>On Tue, Mar 8, 2011 at 5:02 PM, Arnaud Delorme <<a href="mailto:arno@ucsd.edu" target="_blank">arno@ucsd.edu</a>> wrote:<o:p></o:p></p></div><div><div><blockquote style='border:none;border-left:solid #CCCCCC 1.0pt;padding:0in 0in 0in 6.0pt;margin-left:4.8pt;margin-right:0in'><div><p class=MsoNormal>Dear Hui,<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>as the warning says, this is probably due to some problem with Matlab when computing the rank of your Matrix. You should upgrade to a newer Matlab version and this should be fine.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Best regards,<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Arno<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p><div><div><p class=MsoNormal>On Feb 4, 2011, at 12:39 PM, hui zhang wrote:<o:p></o:p></p></div><p class=MsoNormal><br><br><o:p></o:p></p><p class=MsoNormal>Hi,<br><br>When using the function of pop_runica to run ica on epoched data, I got the following message: <br><i><br>Attempting to convert data matrix to double precision for more accurate ICA results.<br>Warning: fixing rank computation inconsistency (39 vs 38) most likely because running under Linux 64-bit MatlabData rank (39) is smaller than the number of channels (62).<br><br>Input data size [39,654000] = 39 channels, 654000 frames/nAfter PCA dimension reduction,<br> finding 39 ICA components using extended ICA.</i><br><br>The matlab code: <br><i>eeglab;<br>EEG = pop_loadset('filename',filename);<br>EEG = pop_runica(EEG, 'icatype', 'runica', 'chanind',[2:63], 'extended', 1);</i><br><br>I ran this code on datasets of previous project, it seems fine. <br>The version of eeglab installed on my computer is: eeglab9.0.1.2b.<br><br>Does anyone have any idea on this? Is there any ways I can turn the pca off in my code? <br><br>Thanks in advance!<br><br>-Hui<br>_______________________________________________<br>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu" target="_blank">eeglablist-unsubscribe@sccn.ucsd.edu</a><br>For digest mode, send an email with the subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><o:p></o:p></p></div><p class=MsoNormal><o:p> </o:p></p></div></div></div><p class=MsoNormal><br>_______________________________________________<br>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu" target="_blank">eeglablist-unsubscribe@sccn.ucsd.edu</a><br>For digest mode, send an email with the subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><o:p></o:p></p></blockquote></div></div></div><p class=MsoNormal><o:p> </o:p></p></div></div><p class=MsoNormal><o:p> </o:p></p></div></body></html>