[Eeglablist] Frequency-time spectrogram deconstruction

[Makoto Miyakoshi]

Dear Panos,

Welcome to the time-frequency world.

> Would I just need to bandpass filter my post-processed EEG signal to each
frequency range of interest (i.e., alpha: 8-12Hz etc) and then plot the
remaining EEG signal over time, or is there another way to do this?

That's one way to go. Nothing is wrong with that!

More convenient and established way to go is to perform time-frequency
transform using short-term Fourier transform or Wavelet transform. Google
EEGLAB time-frequency and you'll find many of our past workshop materials.
For example, see Slide 21 of this file
https://sccn.ucsd.edu/mediawiki/images/a/a6/C2_A3_Time-frequencyDecAndAdvancedICAPracticum_updateJan2017.pdf

You can also obtain bin-mean values from power spectral density. See below.
https://sccn.ucsd.edu/wiki/Makoto's_useful_EEGLAB_code#How_to_extract_EEG_power_of_frequency_bands

Makoto

On Mon, Jan 7, 2019 at 1:34 AM Fotiadis, Panagiotis <
Panagiotis.Fotiadis at pennmedicine.upenn.edu> wrote:

> Hello,
>
>
> I am fairly new to EEGLab and I had a question concerning the
> deconstruction of my EEG signal into its alpha/beta/theta/delta
> sub-components:
>
>
> After pre-processing some subjects with EEG data from 128 channels and
> performing ICA (using runica), I used eeglab and chronux to plot the
> power/frequency and frequency/time spectrograms of several epochs of
> interest.
>
>
> Is there a way to extract the alpha/beta/theta/delta frequencies of those
> epochs and quantify when they occur in time? I can visualize when each type
> of neuronal oscillation occurs by looking at the overall
> frequency/time spectrogram, but I was wondering whether there was a more
> robust way to actually plot each type of oscillation separately and/or
> quantify when it occurs.
>
>
> Would I just need to bandpass filter my post-processed EEG signal to each
> frequency range of interest (i.e., alpha: 8-12Hz etc) and then plot the
> remaining EEG signal over time, or is there another way to do this?
>
>
> Thank you in advance!
>
>
> Best,
>
> Panos
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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