[Eeglablist] Merging of Channel Time Series

[Makoto Miyakoshi]

Dear Ronald and Simon,

I completely agree with Simon. Use pcschash() function for dimension
reduction, like

[eigenvectors,eigenvalues] = pcsquash(EEG.data(:,:), 15);

Makoto


2014-05-05 10:27 GMT-07:00 Simon Kamronn <simon at kamronn.dk>:

> Hi Ronald,
>
>
>
> Depending on your specific application, I would just use PCA and then
> retain the 15 components with largest eigenvalues. Alternatively you could
> interpolate new channels from the old.
>
>
>
> Best,
>
> Simon
>
>
>
> *Fra:* eeglablist-bounces at sccn.ucsd.edu [mailto:
> eeglablist-bounces at sccn.ucsd.edu] *På vegne af *Anderson, Ronald C
> *Sendt:* 4. maj 2014 09:18
> *Til:* eeglablist at sccn.ucsd.edu
> *Emne:* [Eeglablist] Merging of Channel Time Series
>
>
>
> All:
>
>
>
> As part of an EEG time series analysis, our team would like to combine
> multiple channels of data into a single time series. As an example, taking
> 60 channels of EEG data and merging certain channels to make a set of 15
> channels. Several analyses would be conducted on the merged data, including
> coherence, power, SL, and GC. I am writing to the EEGLAB list serve to see
> if anyone has suggestions for a best-practice method of integrating several
> channels into a representative time series.
>
>
>
> We thank you for any assistance you can provide. My apologies if this
> question seems simplistic or has already been addressed on the list serv.
>
>
>
> Regards,
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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