[Eeglablist] how to do group analysis in SIFT?

[Tim Mullen]

Also, you should check out the LIMO
EEG<https://gforge.dcn.ed.ac.uk/gf/project/limo_eeg/>plugin for EEGLAB
(C. Pernet ... G. Rousellet), which affords a number of
statistical tests including mass-univariate analysis (GLM) and
t-tests/ANOVAs/ANCOVAs with bootstrapping, trimming, etc. The GLM approach
with spatiotemporal clustering for multiple comparisons correction may be
particularly useful for testing the large number of hypotheses associated
with the time-freq connectivity matrices produced by SIFT.

Tim


2012/4/9 Tim Mullen <mullen.tim at gmail.com>

> Hui-bin Jia,
>
> I would not attempt to fit a single model to the collection of trials
> taken across all subjects. Fitting an MVAR model to an ensemble of trials
> assumes that each trial is a realization from the same stochastic process
> (e.g. all trials have the same statistical properties). While this is often
> a reasonable assumption for a single subject, significant differences in
> channel data statistics are likely to exist between subjects, e.g. due to
> differences in sensor impedance, noise, biophysics, neural architecture,
> etc. Besides, fitting a single model to data from all subjects presumes
> that the optimal model order is the same across subjects, which is probably
> not true.
>
> Instead, you should fit a separate model for each subject (using only the
> channels of interest, as you specified). If for each subject you have (the
> same) M channels (e.g. [11, 12, 13]), T time-points, and F frequencies,
> this will produce connectivity matrices in EEG.CAT.Conn.(methodname) of
> dimension [M x M x F x T] for each subject. It then remains to simply use
> your preferred statistical test to assess whether mean population
> connectivity for various channel-pairs, times, and frequencies is
> significantly non-zero or whether there is are differences in population
> means across subject groups or conditions. For this, if the population is
> large, it's probably suitable to use an ANOVA or t-test, preferably with
> bootstrap resampling (see statcond() in EEGLAB) and/or trimming (Yuen test)
> if outliers are present. If the sample size is not very large, or the
> sample distributions significantly non-normal, you might also consider
> other non-parametric tests such as Mann-Whitney U or permutation testing
> depending on nature of the hypothesis you are wishing to test. You'll need
> to correct for multiple comparisons e.g. across time and frequency. For
> that, you might apply the fdr() function in EEGLAB to obtain corrected
> p-values for the population-level significance test.
>
> Individual subject (e.g. first-level, not group) statistics including
> bootstrap, jackknife, phase randomization, etc can be carried out in SIFT
> using the various routines prefixed by "stat_" or "pop_stat_". In
> particular, check out these functions:
>
> pop_stat_analyticStats.m
> pop_stat_surrogate.m
> pop_stat_surrogateStats.m
>
> Use the "doc <function-name>" feature of Matlab for info on how to use
> these functions.
>
> Tim
>
> 2012/4/6 Hui-bin Jia <420247417 at qq.com>
>
>>  Hi, everyone
>>
>>    SIFT doesn’t have Group Analysis module in current version.
>>    But I want to use this toolbox to do the granger-causal connectivity
>> analysis of channel data.
>>    To achieve this goal, I thought out this method. But I don’t know
>> whether it right or not.
>>
>>    I will make use of the experiment in the SIFT manual to illustrate my
>> method.
>>    Assuming I have got the data of 10 subjects in two conditions
>> (RespWrong and RespCorrect).
>>    So there are 20 datasets in total. Independent component analysis has
>> been conducted on all of the 20 datasets.
>>    In each dataset, the number of channels and independent components
>> both are 152(nbchan = 152 ), and the   number of epochs is 123. In every
>> epoch, there are 1024 data points(ie. pnts = 1024).
>>
>>    Then I collected the EEG data of the eleventh subject in the two
>> conditions (RespWrong and RespCorrect).
>>    And these two dataset are called ‘datasetwrong’ and ‘datasetcorrect’.
>> Independent component analysis has
>>    been conducted on all of the 2 datasets. In each dataset, the number
>> of channels and independent components
>>    both are 152(nbchan = 152 ), and the number of epochs is 1030. In
>> every epoch, there are 1024 data
>>    points(ie. pnts = 1024).
>>
>>    Now I want to do granger-causal connectivity analysis of the RespWrong
>> condition.
>>    The No. of channels for analysis are 11, 12, and 13. For the first ten
>> subjects, I get 1230(123*10) epochs
>>    at each channel.
>>    1. I get 1000 epochs which are free from artifacts from all the 1230
>> epochs(123*10) in channel 11, 12 and 13.
>>    Then using these data, I get 3 1024*1000 matrices.
>>    They are called a, b, and c. And I assume these three matrices
>> represent the data from all of the first ten
>>    subjects.
>>    2.'datasewrong' is loaded in MATLAB, and 30 epochs in this dataset are
>> deleted, which means
>>    the number of the remaining epochs is 1000.
>>    3, in the command line,
>>    I type EEG.data(11,:,: ) = a ; EEG.data(12,:,: ) = b; EEG.data(13,:,:
>> ) = c.  EEG.data is a 152*1024*1000 matrix.
>>    4. In the command line, I type EEG.data = EEG.icaact; EEG.icawinv =
>> EEG.icaweights = ones(152,152).
>>    5, I select the eleventh, twelfth, and thirteenth independent
>> components and do granger-causal connectivity
>>    analysis according to the Data processing pipeline illustrated by the
>> SIFT manual.
>>
>>    I’m asking my method is right or not? Can it be considered as an
>> alternative for group analysis?
>>
>>    Can I do bootstrap sample and phase randomization with the current
>> version of SIFT?
>>
>>    Thanks!
>>
>>
>> Sincerely,
>> Hui-bin Jia
>>
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>
>
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