[Eeglablist] Spectrum with good frequency resolution for short time windows?

[Marco Simões]

Christian,

You might also want to try zero padding your signals. In principle, I think it will provide a better frequency resolution.

Best regards,

Marco Simões

A 03/03/2012, às 00:11, Tim Mullen escreveu:

> Christian,
> 
> You might try multi-taper FFTs, which has superior bias-variance
> tradeoffs than a single-tapered FFT or wavelets for small-sample data.
> I believe newtimef() contains suitable options for multi-taper
> spectral estimation, so no need to fuss with external functions.
> Otherwise, check out Chronux (www.chronux.org) function mtspecgramc()
> and related mt* functions (which also compute analytic/jackknife
> confidence intervals, etc).
> 
> AR modeling can also be a good choice when faced with a small-sample
> situation, if the spectrum has not too many degrees of freedom (few
> spectral peaks) and you can deduce the correct model order (in fact,
> an appropriately fit AR model is an optimal (maximum entropy) choice
> for a spectral estimator IF you know the correct model order -- which
> of course you don't, a priori, with EEG). Sometimes/often
> multi-tapering can be a better choice than (unregularized) AR models.
> 
> FYI: while there are many choices of excellent functions for AR
> spectral estimation (including Matlab's own routines), the SIFT plugin
> for EEGLAB (sccn.ucsd.edu/wiki/SIFT) also contains routines for
> maximum-entropy (e.g. vieira-morf) VAR model fitting using a sliding
> window (pop_est_fitMVAR() ; single trial, or across multiple trials
> for reduced variance) or adaptive estimation via Kalman filtering
> (est_fitMVARKalman() ;  no sliding window, you get a spectral estimate
> for each sample point). Once model is fit, just select the spectral
> density as one of the desired measures in pop_est_mvarConnectivity().
> The auto-spectra and cross-spectra (optionally converted to dB) will
> be stored, respectively, on the main diagonal and off-diagonals of
> EEG.CAT.Conn.S  (this is of dimension [channel x channel x time x
> frequency]) and the corresponding times and frequencies (to insert
> into study structure) are stored in EEG.CAT.Conn.erWinCenterTimes and
> EEG.CAT.Conn.freqs
> 
> In general, if you want to incorporate your ERSP results computed
> externally into a study design, I suppose you would want to insert
> these into the appropriate sub-fields of   STUDY.cluster   (e.g.
> 'erspdata', 'ersptimes', etc). See also
> http://sccn.ucsd.edu/wiki/Chapter_07:_EEGLAB_Study_Data_Structures.
> 
> Tim
> 
> 
> On Fri, Mar 2, 2012 at 1:32 AM, Christian Scharinger
> <c.scharinger at gmx.net> wrote:
>> Hi there,
>> 
>> I want to calculate a spectrum  for a rather short time window (500 ms
>> or less). But nevertheless I want to have a good frequency resolution
>> (0.5 Hz or better).
>> 
>> My first idea was to concatenate several of this short time windows and
>> then run a FFT over the resulting larger time window. But I guess this
>> approach could be problematic?
>> 
>> My second idea was to use an AR-model instead of FFT to calculate the
>> spectral power. This works quite well. But the problem here is how to
>> (re-)integrate this AR-spectrum that I calculated "outside" eeglab later
>> in an eeglab study design.
>> Is there any possibility to replace the spectra that are calculated in
>> eeglab with AR-spectra?
>> 
>> Maybe someone has any suggestions or ideas for this problem or maybe a
>> completely different (better) solution?
>> 
>> Many thanks in advance,
>> 
>> Christian
>> 
>> --
>> Christian Scharinger, M.A.
>> 
>> Knowledge Media Research Center (KMRC)
>> Tuebingen
>> 
>> 
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> 
> 
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