[Eeglablist] ERSP parametres

[Makoto Miyakoshi]

Dear Daniele,

See slide 21
http://sccn.ucsd.edu/mediawiki/images/1/19/C2_A3_Time-frequencyDecAndAdvancedICAPracticum.pdf

Also check this out
http://sccn.ucsd.edu/mediawiki/images/6/68/PlenaryDay1_EEGLAB_TFAnalysis.pdf

Personally, I would not use such a narrow frequency window as 4-13. If your
epoch is -1 to 7 sec long, use [3 0.85] for the cycle and 'freq', [3 100],
'nfreqs', 100 to start with.

Makoto

On Mon, May 11, 2015 at 8:55 AM, Daniele D. Marasco <
daniele.marasco at gmail.com> wrote:

> Dear list,
>
> I'm trying to perform my first TF analisys (study-->precomp-->ersp), but
> I'm not sure about the parametres to use. My ephochs are  -1000 to 7000 ms
> (512 Hz s.r.). I'm interested in theta-alpha bands and I'd like to perform
> only one analysy (preliminarly... at least).
>
> I'm using these parametres:
>
> 'cycles',[2 0.5], 'freqs',[4 13], 'nfreqs', 100, 'ntimesout', 400,
> 'freqscale','linear'
>
> In this way I have just obtained (via TEST):
>
> Computing Event-Related Spectral Perturbation (ERSP) and
>   Inter-Trial Phase Coherence (ITC) images based on 20 trials
>   of 4096 frames sampled at 512 Hz.
> Each trial contains samples from -1000 ms before to
>   6998 ms after the timelocking event.
>   Image frequency direction: normal
> Using 2 cycles at lowest frequency to 3.25 at highest.
> Generating 200 time points (-721.7 to 6719.7 ms)
> Finding closest points for time variable
> Time values for time/freq decomposition is not perfectly uniformly
> distributed
> The window size used is 285 samples (556.641 ms) wide.
> Estimating 100 linear-spaced frequencies from 4.0 Hz to 13.0 Hz.
> Processing time point (of 200): 10 20 30 40 50 60 70 80 90 100 110 120
>  130 140 150 160 170 180 190 200
> Computing the mean baseline spectrum
> Note: Add output variables to command line call in history to
>       retrieve results and use the tftopo function to replot them
>
> What do you think about? Could you suggest me some ideas?
>
> Thanks so much!
> Daniele
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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