[Eeglablist] EEG Alpha Asymmetry
Alexander J. Shackman
shackman at wisc.edu
Wed Aug 8 06:43:18 PDT 2007
typically the spectral power density (uV2/Hz) estimates for alpha (8-13Hz)
at each of the homologous channels are computed using overlapping (50-75%)
hanning or hamming windowed chunks and log_e transformed before subtraction.
different results will likely arise as a function of the reference montage
(avg, ears, cz, mastoids).
see also the articles @
http://psyphz.psych.wisc.edu/%7Eshackman/EEG_ERP_Methodology.htm#Anterior_Cerebral_Asymmetry
and
http://psyphz.psych.wisc.edu/%7Eshackman/EEG_ERP_Methodology.htm#Average_Reference
and
http://psyphz.psych.wisc.edu/web/pubs/2004/Electrode_density.pdf
i took a look at the spectopo function it allows overlapping and
logging...i'm not certain how one would do the windowing, although it looks
(see below) that the default behavior of the pwelch function is to use a
hamming window.
% spectopo() - Plot the mean log spectrum of a set of data epochs at all
channels
% as a bundle of traces. At specified frequencies, plot the
relative
% topographic distribution of power. If available, uses
pwelch() from
% the Matlab signal processing toolbox, else the EEGLAB spec()
function.
% Plots the mean spectrum for all of the supplied data, not
just
% the pre-stimulus baseline.
% Usage:
% >> spectopo(data, frames, srate);
% >> [spectra,freqs,speccomp,contrib,specstd] = ...
% spectopo(data, frames, srate, 'key1','val1',
'key2','val2' ...);
% Inputs:
% data = If 2-D (nchans,time_points); % may be continuous = a single
epoch,
% else a set of concatenated data epochs. Else, a 3-D set of
data
% epochs (nchans,frames,epochs)
% frames = frames per epoch {default|0 -> data length}
% srate = sampling rate per channel (Hz)
%
% Optional inputs:
% 'freq' = [float vector (Hz)] vector of frequencies at which to plot
power
% scalp maps, or else a single frequency at which to plot
component
% contributions at a single channel (see also 'plotchan').
% 'chanlocs' = electrode locations file or EEG.chanlocs structure. For
format, see
% >> topoplot example
% 'limits' = axis limits [xmin xmax ymin ymax cmin cmax] set x y and
color axis
% limits. May omit final values or use NaN's
% Ex: [0 60 NaN NaN -10 10], [0 60], ...
% Default color limits are symmetric around 0 and are
different
% for each scalp map {default|all NaN's: from data}
% 'title' = [quoted string] plot title {default: none}
% 'freqfac' = [integer] ntimes to oversample -> frequency resolution
{default: 2}
% 'nfft' = [integer] length to zero-pad data to. Overwrites 'freqfac'
above.
% 'winsize' = [integer] window size in data points {default: from data}
% 'overlap' = [integer] window overlap in data points {default: 0}
% 'percent' = [float 0 to 100] percent of the data to sample for
computing the
% spectra. Values < 100 speed up the computation. {default:
100}.
% 'freqrange' = [min max] frequency range to plot. Changes x-axis limits
{default:
% 1 Hz for the min and Nyquist (srate/2) for the max. If
specified
% power distribution maps are plotted, the highest mapped
frequency
% determines the max freq}.
% 'reref' = ['averef'|'off'] convert input data to average reference
% {default: 'off'}
% 'mapnorm' = [float vector] If 'data' contain the activity of an
independant
% component, this parameter should contain its scalp map. In
this case
% the spectrum amplitude will be scaled to component RMS
scalp power.
% Useful for comparing component strengths {default: none}
% 'boundaries' = data point indices of discontinuities in the signal
{default: none}
% 'plot' = ['on'|'off'] 'off' -> disable plotting {default: 'on'}
% 'rmdc' = ['on'|'off'] 'on' -> remove DC {default: 'off'}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
but
>> help pwelch
PWELCH Power Spectral Density estimate via Welch's method.
Pxx = PWELCH(X) returns the Power Spectral Density (PSD) estimate,
Pxx, of a discrete-time signal vector X using Welch's averaged,
modified periodogram method. By default, X is divided into eight
sections with 50% overlap, each section is windowed with a Hamming
window and eight modified periodograms are computed and averaged.
hth, alex shackman
On 8/8/07, Arnaud Delorme <arno at salk.edu> wrote:
>
> Dear Gregor,
>
> first you need to compute alpha power. To do so may use the function
> spectopo or newtimef to compute the alpha power. For example for a
> single channel on the left and on the right.
>
> x = 10; % example of channel index on the left
> y = 11; % example of channel index on the right
> [ spec_x freqs ] = spectopo(EEG.data(x,:), EEG.pnts, EEG.frames, 'plot',
> 'off');
> [ spec_y freqs ] = spectopo(EEG.data(y,:), EEG.pnts, EEG.frames, 'plot',
> 'off');
> [tmp alpha_ind] = min( abs(freqs-10)); % find closest frequency to alpha
> at 10 Hz
> asymmetry = spec_x(alpha_ind) - spec_y(alpha_ind) % I think asymmetry is
> about subtracting power but I might be wrong.
>
> Hope this helps.
> Best regards,
>
> Arno
>
> gkohls at ukaachen.de wrote:
> > Dear all,
> >
> > I collected resting EEG to analyze frontal Alpha asymmetry. Can anyone
> > help me how to analyze alpha band and asymmetries with EEGlab? I tried
> > but did not succeed.
> >
> > Many thanks in advance,
> > Gregor
>
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--
Alexander J. Shackman
Laboratory for Affective Neuroscience
Waisman Laboratory for Brain Imaging & Behavior
University of Wisconsin-Madison
1202 West Johnson Street
Madison, Wisconsin 53706
Telephone: +1 (608) 358-5025
FAX: +1 (608) 265-2875
EMAIL: shackman at wisc.edu
http://psyphz.psych.wisc.edu/~shackman
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