<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Susana and Ingrid,<div><br></div><div>designing efficient filters at very low frequency can be challenging. Below is a solution using pure Matlab code and elliptic filters for high pass filtering at 0.1 Hz (transition bandwidth is from 0.05 to 0.1 Hz).</div><div><i>EEG.srate</i> is the sampling rate, <i>EEG.nbchan</i>, the number of data channel and <i>EEG.data</i> contains the continuous data (number of channels x number of data points). The code below requires the signal processing toolbox.</div><div>You may also notice that I <i>detrend</i> the data first. This is to remove any DC offset and very slow drifts that tend to generate more artifacts at data boundaries.</div><div>The function <i>filtfilt</i> applies the filter in both direction (forward then backward) to avoid phase distortions.</div><div><br></div><div>Hope this helps,</div><div><br></div><div>Arno</div><div><br></div><div><i>[N, Wn] = ellipord(.1/(EEG.srate/2), .05/(EEG.srate/2), 0.5, 3)<br>[b a] = ellip(N, 0.5, 3, Wn, 'high');<br><br>for index = 1:EEG.nbchan<br> EEG.data(index,:) = detrend(EEG.data(index,:));<br> EEG.data(index,:) = filtfilt( b, a, EEG.data(index,:));<br>end;</i></div><div><i><br></i></div></body></html><p>----------------------------------</p>
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