Hi Stavros,<br><br>Thanks a lot for the PCA function! <br><br>Before you apply the PCA by princomp(), do you re-reference the data to the averaged-reference? We're using 128 recording system, and I found that the raw data from channels close to the eyes are much larger(at 50~100 micro volt scale) than the data from channels near Cz (<1 scale). I suspect the scale differences between channels may influence the PCA performance such that small PCs may account for the channels with small scale. By re-reference the data, channels near Cz change the scale to ~10 micro volts. Also, Matlab suggests we could use princomp(zscore(X)) to rescale the data. What do you think using z-score? <br>
<br>best<br><br><div class="gmail_quote">2012/1/19 stauros dimitriadis <span dir="ltr"><<a href="mailto:stdimitriadis@yahoo.gr">stdimitriadis@yahoo.gr</a>></span><br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div><div style="color:#000;background-color:#fff;font-family:times new roman,new york,times,serif;font-size:12pt"><div>Dear Chang</div><div>Since it is important to reduce the dimensionality of your data by adopting PCA, a simple rule is to select </div>
<div>an appropriate number of principal components regarding the variance of your data.</div><div><br></div><div>In MATLAB script:</div><div><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
[pc,score,latent,tsquare] = princomp( (time x triasl) x channel); % concatenate your trials in time (suggested)</pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
r=find(cumsum(latent)./sum(latent) > 0.98);</pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
%r(1) is the total number of components need to capture more than 98 % of the variance of </pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
%your initial data</pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br></pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Afterward, proceed with the ICA.</pre>
<pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px"><br></pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
I hope that i help you !!</pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br></pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px"><br></pre>
<pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px"><br></pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
Best regards</pre><pre style="margin-top:1ex;margin-bottom:0.7ex;margin-left:32px;font-family:'courier new',courier,monospace;font-size:12px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Stavros Dimitriadis</pre>
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