[Eeglablist] EEG 'localizer'?

[Makoto Miyakoshi]

Dear Johan,

Very very interesting attempt! I hope someone on this list give you good
ideas. Let me start with my humble opinions.

Visual and auditory should be quite easy since they are pretty robust and
fast. Motor, it depends on what you mean. If you say just pressing a button
is sufficient, it should not be too difficult. Go-nogo, you need to divide
the trials into two categories, which doubles the number of trials.

In any case, the associated ERPs have short time constants (300-400 ms) so
within 10 min (=600 sec) recording at least 60-80 trials for each condition
should work. If you need to combine them, you should be able to treat them
as (theoretically) orthogonal and analyzed as such. However, realistically
you want to consider the additional effect, such as attention switching
across modalities, probability (avoid oddball effect!) etc.

Makoto

On Tue, Dec 23, 2014 at 7:53 AM, Johan <johanvandermeer at gmail.com> wrote:

> Dear all,
>
> I am searching for a single task, which can (well.. ideally..) be
> performed in under about 5-10 minutes (no longer), and which can get me
> (ideally!) an ERP of:
>
> visual
> auditory
> motor
> go-nogo (response inhibition)
>
> ... and, if possible, also the possibility to impart specific frequencies
> in the EEG signal that can be extracted later on - sort of like the case
> when you have a flickering checkerboard with pattern reversals every 0.2
> seconds for a 5 Hz 'base' signal in the EEG.
>
> The objective is to compress as much information as possible into a single
> EEG task - so that you can compare the outcome (within subject analysis)
> between 2 different situations... without (1) totally confusing the
> subject, and (2) still have the time to do a larger experiment.
>
> So - it's sort of like an EEG 'localizer' task.
>
> Does anyone ever try this before, or has seen anywhere something like this
> being attempted? Or rather you think this is just impossible and not worth
> the effort? :-)
>
> Thank you very much for your time and kind regards,
> Johan van der Meer
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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