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<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span lang="EN-US">Dear list members,<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">I would like to feed machine learning algorithms with virtual sensor data of different frequency bands as well as broad bands (e.g. 1-45Hz, beta-band etc) and would appreciate your help with some issues concerning the
filtering of the data. In order to obtain virtual sensors, I compute lcmv beamformer with bandpass filtered averaged sensor data (e.g. filtered in beta band). After obtaining the spatial filter, I multiply the weights with the single trials filtered in beta
band. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">When checking the frequency spectrum of the beta band filtered average sensor data, the spectrum looks all right (see attached picture “mean sensor data betaband.png). However, when checking the frequency spectrum of
the virtual sensors, the spectrum does not show signal in beta band, but shows a spectrum between 1 and 120 Hz, while signal seems to be missing around 20 Hz (see the other attached picture).
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">I checked all analyzing steps and found that the data covariance matrix already presents a similar frequency spectrum as the virtual sensors. I know too little to understand this issue and would appreciate it if anyone
could help me here. Would it make more sense to filter only the virtual sensors, with no filtering at all beforehand?
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Thanks for your help in advance, best regards,<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Kirsten<o:p></o:p></span></p>
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