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

Harold Cavendish harold.cav89 at gmail.com
Thu Jan 28 13:31:37 CET 2016


Dear Julian,

thank you for your help, I believe I read the same page but I didn't
realise it could be used with ft_timelockstatistics – the way certain ft_
functions fit others is still a bit unclear to me (it's practically my
first contact with FT). Now, it appears to be exactly what I'm looking for.

I took me some time to figure out how to import my already processed data
(ERPs averaged per subject) into FieldTrip and run the test but it seems
I've succeeded. However, ft_timelockstatistics(cfg, data) gave me the
following warnings:


*1. Warning: the data does not contain a trial definition*
*2. Warning: reconstructing sampleinfo by assuming that the trials are
consecutive segments of a continuous*
*recording*
*3. Warning: doing a two-sided test without correcting p-values or
alpha-level, p-values and alpha-level*
*will reflect one-sided tests per tail*

Should I be worried about these? (Here's the full output:
https://gist.github.com/anonymous/1866a2b1a424dd9f0ac9)

Finally, the resulting *stat* structure contains 10 fields but I'm not sure
what *mask*, *stat* and *ref* represent. Is there any guidance on how to
report the results like in the case of comparisons between conditions (
http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test
)?

Many thanks!
Harold


2016-01-26 6:35 GMT+01:00 Julian Keil <julian.keil at gmail.com>:

> Dear Harold,
>
> have you checked out ft_statfun_correlationT?
> This sounds like the function you're looking for. Use cfg.statistic =
> 'ft_statfun_correlationT' in your call to ft_timelockstatistics.
> Plus you can use all the correction methods available in FT.
> See the FAQ for more info:
> http://www.fieldtriptoolbox.org/faq/how_can_i_test_for_correlations_between_neuronal_data_and_quantitative_stimulus_and_behavioural_variables
>
> Good luck,
>
> Julian
>
> ********************
> *Dr. Julian Keil*
>
> AG Multisensorische Integration
> Psychiatrische Universitätsklinik
> der Charité im St. Hedwig-Krankenhaus
> Große Hamburger Straße 5-11, Raum A007
> 10115 Berlin
>
> Telefon: +49-30-2311-1879
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>
> http://psy-ccm.charite.de/forschung/bildgebung/ag_multisensorische_integration
>
> Am 26.01.2016 um 00:25 schrieb Harold Cavendish:
>
> 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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