[Eeglablist] continuous covariate

[Alexandre Obert]

Thank you for the references!
I think that computing event-related regression coefficients (as in the 
Hauk's paper) seems easier but quite complicated for the beginner I am...
I don't really understand how they technically did this and how I could 
apply this to my data.

In fact, I conducted a study which contains several sentences split into 
2 conditions. I observed a larger P600 for the condition A than for the 
condition B at the end of the sentences (time window set a the last word).
What I would like to test is the effect of a feature (such as the 
length) of the sentences for each condition separately on the amplitude 
of the P600.

My first idea was to compute the mean of amplitude in the 600-900 
time-window for each sentence across subject and use them as dependent 
measure and length values as predictors.
But after reading references, it seems not statistically acceptable, right ?

Alexandre

Le 23/01/2016 00:33, Stephen Politzer-Ahles a écrit :
> Yes, this can easily be done with single-trial analysis / 
> event-related regression coefficient, or similar analyses. See, e.g., 
> Hauk et al. 2006 in NeuroImage, and Smith & Kutas 2015 in 
> Psychophysiology.
>
>
>
> ---
> Stephen Politzer-Ahles
> University of Oxford
> Language and Brain Lab
> Faculty of Linguistics, Phonetics & Philology
> http://users.ox.ac.uk/~cpgl0080/ <http://users.ox.ac.uk/%7Ecpgl0080/>
>
> On Fri, Jan 22, 2016 at 1:19 PM, Alexandre Obert 
> <obert.alexandre at gmail.com <mailto:obert.alexandre at gmail.com>> wrote:
>
>     Dear all,
>
>     I wonder if there is a way to assess the effect of a continuous
>     variable from items' features on erps amplitudes (without
>     categorized it such as using median-split) ?
>     For the ones who know fMRI, I would like compute something similar
>     to the parametric modulation...
>
>
>     Regards,
>
>     Alexandre Obert
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