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<p>Hi Nathan, <br>
</p>
<p>I completely agree with what Jan-Mathijs wrote. Just a simple
addition to improve performance: You can build your decoder or
Fourier amplitudes with harmonics but you can alternatively use
CCA with a sines and cosines at the appropriate frequencies as
reference signals to transform your time series EEG signal. With
MEG and EEG we and others found clear performance increases for
CCA over FT (see reference below). CCA is to my knowledge
available in fieldtrip.</p>
<p>Best, <br>
</p>
<p> Jochem <br>
</p>
<p>Hakvoort, G., Reuderink, B., and Obbink, M. (2011). Comparison of
PSDA
<br>
and CCA Detection Methods in a SSVEP-Based BCI-System. (CTIT
Technical
<br>
Report Series; No. TR-CTIT-11-03) Enschede: Centre for Telematics
and
<br>
Information Technology University of Twente.</p>
<p>Reichert, C., Kennel, M., Kruse, R., Hinrichs, H., & Rieger,
J. W. (2013). Efficiency of SSVEF Recognition from the
Magnetoencephalogram. In Proceedings of the International Congress
on Neurotechnology, Electronics and Informatics (BrainRehab-2013),
pages 233-237, DOI: 10.5220/0004645602330237 <br>
</p>
<p><br>
</p>
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----------------------------------------------------------------------<br>
<br>
Message: 1<br>
Date: Thu, 23 Feb 2023 11:12:43 +0000<br>
From: "Schoffelen, J.M. (Jan Mathijs)"<br>
<<a
href="mailto:janmathijs.schoffelen@donders.ru.nl"
target="_blank" moz-do-not-send="true">janmathijs.schoffelen@donders.ru.nl</a>><br>
To: FieldTrip discussion list <<a
href="mailto:fieldtrip@science.ru.nl" target="_blank"
moz-do-not-send="true">fieldtrip@science.ru.nl</a>><br>
Subject: Re: [FieldTrip] [Fieldtrip] Analysis Options for
SSVEPs<br>
Message-ID: <<a
href="mailto:6D6ED6A7-9FC7-433E-AC7D-BD34E613C54F@donders.ru.nl"
target="_blank" moz-do-not-send="true">6D6ED6A7-9FC7-433E-AC7D-BD34E613C54F@donders.ru.nl</a>><br>
Content-Type: text/plain; charset="utf-8"<br>
<br>
Hi Nathan,<br>
<br>
This question is not at all dumb, it’s good one!<br>
<br>
A classical SSVEP analysis indeed estimates the power of
the evoked response (i.e. first average the trials, then
compute the power spectrum). This works well under the
valid assumption that the timing of the individual stimuli
is 1) very precise, and 2) not jittered across trials.<br>
<br>
Now, if you want to estimate time-varying changes in the
evoked power - e.g. to use it as a ‘marker’ for the
attentional focus - you need to keep in mind that the
specificity of your spectral estimate (+your sensitivity
to detect changes over time) (i.e. the extent to which
the estimate at - say - 15 Hz reflects only the energy in
the signal at 15 Hz or that it also contains energy of
close by frequencies -> a phenomenon that relates to
the notion of spectral leakage, and the time-frequency
trade off) depends on your experimental stimuli and your
analysis parameters. In your example, using frequencies of
13 and 15 Hz, they may not be sufficiently far apart in
the spectral domain in order to be sufficiently reliably
resolved given your most likely choice sliding time window
that you are going to use for your analysis.<br>
<br>
Concretely, the kernel that you will use for your
time-frequency analysis should have a bandwidth that is
small enough to be able to separate the stimulus
frequencies. This requires a sliding time window width of
at least one second (because a time window of 1 second
yields a spectral resolution of 1 Hz), which is probably
too long for your experimental design (because it would
require trials that are correspondingly much longer than
that time window obviously). Therefore, I suspect that you
may need to think a bit (and discuss with your co-workers)
the optimal stimulation frequencies for both hemispheres.<br>
<br>
Another thing to keep in mind w.r.t. the optimal
frequencies, is that ideally the lower order harmonic
frequencies of the stimuli don’t overlap, because it may
be interesting later on to look at the power of the
harmonics as well, and in this case those harmonics will
still distinguish between stimuli.<br>
<br>
Good luck with your experiment,<br>
<br>
Jan-Mathijs<br>
<br>
<br>
> On 23 Feb 2023, at 10:35, Nathan Han via fieldtrip
<<a href="mailto:fieldtrip@science.ru.nl"
target="_blank" moz-do-not-send="true">fieldtrip@science.ru.nl</a>>
wrote:<br>
> <br>
> Hi all,<br>
> <br>
> I'm still quite a novice with Fieldtrip and signal
processing so please forgive me if this question is dumb
:)
<br>
> <br>
> I'm running an experiment where there is a visual
stimulus on the left and right side of the screens, the
purpose of which is to elicit SSVEPs. One circle flickers
at 13Hz (e.g., the left side) and the other flickers at
15Hz (e.g., the right side). I would like to analyse the
change in visual spatial attention over the course of the
trial and one way I was thinking was that if attention
switches from the left to the right side of the screen,
that 13Hz power would reduce while 15Hz power would
increase over the course of the trial.<br>
> <br>
> I'm not sure if that even makes sense or if it is
possible. If it does, I would like to ask how should I
approach this analysis?<br>
> <br>
> Kind regards,<br>
> Nathan<br>
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