[FieldTrip] Fwd: Identify a window of best correlation between ERP and behavioural data

[Harold Cavendish]

Dear Steve,

thank you for your help, the references are greatly appreciated! Indeed,
I'd like to extend the analysis to identifying parts where the correlation
is significantly different; I am going to try this approach.

Best regards,
Harold

2016-01-26 12:08 GMT+01:00 Stephen Politzer-Ahles <
stephen.politzer-ahles at ling-phil.ox.ac.uk>:

> Hi Harold,
>
> If you're only interested in identifying which timepoints have the best
> correlation, multiple comparisons aren't really an issue; if you're not
> worried about testing for significance, then the place where you see the
> highest estimate is the highest. See e.g., Hauk et al. 2006 in NeuroImage,
> and Smith & Kutas 2015 in Psychophysiology, for similar analyses.
>
> If you're interested in finding a part of the waveform where the
> correlation is significantly different from zero while also dealing with
> multiple comparisons, you could use [spatio]temporal clustering (Maris &
> Oostenveld 2007). Just make an event-related regression coefficient (as
> described in Hauk et al.; just do a regression at each timepoint, and then
> rather than plotting the ERP amplitude at each timepoint, plot the
> regression estimate (*b*) at each timepoint, to derive a similar
> waveform) and then test that waveform against 0 using temporal clustering.
>
> Best,
> Steve
>
>
>
>
> ---
> Stephen Politzer-Ahles
> University of Oxford
> Language and Brain Lab
> Faculty of Linguistics, Phonetics & Philology
> http://users.ox.ac.uk/~cpgl0080/
>
>
>
>> Message: 5
>> Date: Tue, 26 Jan 2016 00:25:13 +0100
>> From: Harold Cavendish <harold.cav89 at gmail.com>
>> To: fieldtrip at science.ru.nl
>> Subject: [FieldTrip] Fwd: Identify a window of best correlation
>>         between ERP and behavioural data
>> Message-ID:
>>         <
>> CALOTjvZXPNWHLefXrG6uss7uF3kkXie7qjiWqEcesOUqWay6qA at mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Dear FieldTrip users,
>>
>> I'm looking for the best method to analyse the relationship between ERP
>> and
>> behavioural data but all I've been able to find so far are methods to
>> compare ERPs (or more generally time-series).
>>
>> Consider the following example: 20 participants performed a memory-related
>> task in which they had to respond based on the difference between the
>> target stimulus and the probe stimulus. EEG (50 electrodes, about 1000
>> samples at 200 Hz) and responses were recorded; ERPs were then created
>> based on epochs in which the responses were correct. Similarly, response
>> accuracy was averaged over all successful epochs per each subject.
>>
>> The resulting data are therefore: 20 participants x 50 channels x 1000
>> samples (ERP) and 20 averages of accuracy (one value per participant).
>>
>> My objective is to identify which portion of the ERP data best explains
>> (or
>> predicts) the changes in accuracy. In other words, in which time interval
>> is the most significant difference between people who do well in the task
>> and those who don't.
>>
>> So far I've been doing correlations between ERP at each time point and
>> overall accuracy, which is unreliable due to multiple comparisons
>> (correction methods are reportedly too conservative). Nevertheless,
>> certain
>> channels show quite strong correlations in multiple time intervals.
>>
>> How would you approach this problem, please?
>>
>> Many thanks!
>>
>> Best regards,
>> Harold
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