[FieldTrip] Regression of behavioral data against EEG data

[Burcu Bayram]

Thank you very much for your reply!

Would you be so kind to elaborate on why interpreting or averaging 
r-squared values or p-values does not make much sense?  And why 
r-squared values give a very reasonable topography, but one that is so 
much different from the topography of beta values?

Best regards,
Burcu

On 12.12.2022 08:18, Schoffelen, J.M. (Jan Mathijs) via fieldtrip wrote:
> Hi Burcu,
> 
> Conventially, the parameter estimates (i.e. the beta weights) are
> taken to the second level for an inferential statistical test.
> Averaging p-values, or r-squared values is usually not done, and also
> does not make much sense.
> 
> Best wishes,
> Jan-Mathijs
> 
> 
>> On 10 Dec 2022, at 17:07, Burcu Bayram via fieldtrip 
>> <fieldtrip at science.ru.nl> wrote:
>> 
>> Dear FieldTrip community,
>> 
>> I'm new to regression analysis of EEG data and unsure which regression 
>> outputs to use. Beta-coefficients give a very different pattern of 
>> results compared to r squared values or p-values, although all of them 
>> (to my understanding) should express a form of relation between the 
>> two datasets. We are looking for help regarding the interpretation of 
>> those data and which one to select for our analysis. In our model, 
>> behaviour is the predictor and EEG activity is the outcome. The 
>> datapoints for each are single experimental trials (~2000 per 
>> subject).
>> So far, we just used simple linear regression, but the plan is to use 
>> multiple linear regression at a later stage.
>> The idea is to plot and interpret the regression results as if they 
>> were EEG amplitudes. So we get a time course and a topography of 
>> regression results, that help us to determine where and when in the 
>> brain behavior predicts neural activity. Our main questions are:
>> 
>> 1. Which value makes most sense to use as an indication of brain/ 
>> behavior relationship? The betas should provide the quality/ direction 
>> of the relationship, but don't say anything about how large or 
>> important that relationship is. The r squared or also the t or p 
>> values for each coefficient tell something about the strength of the 
>> relationship. The issue is, that they give really different activity 
>> patterns. You can see the topographies of beta values, r squared 
>> values and p-values in the attached images.
>> 
>> 2. The second question is which of the single-subject regression 
>> outputs actually can be used for group level plots and statistics: Is 
>> it possible to average over e.g. betas or p-vales across subjects, and 
>> also do group level statistics with (e.g. compare group-level p-values 
>> or betas between two conditions)?
>> 
>> Thank you so much in advance!
>> 
>> Best regards,
>> Burcu
>> <beta_av_postmean.png><p_values_av_postmean.png><r_squared_av_postmean.png>_______________________________________________
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