[Mne_analysis] Baseline correcting pre-stimulus segments for covariance estimation

[Graham Flick]

Hi Denis,

Good point. Here are two images showing the initial segments of the evoked
responses so that you can see the baseline. They show with and without
baseline correction applied, where the baseline window is -4400 to -4300
ms. This doesn't suggest to me that there's any huge deviation from zero
mean without the baseline correction.

In this case, do you think the baseline corrected data show a large enough
improvement for it to be applied... considering the change in the whitened
responses? Or is it safer to just apply the baseline correction to better
ensure that this is zero mean?






On Wed, May 10, 2017 at 9:20 PM, Denis-Alexander Engemann <
denis.engemann at gmail.com> wrote:

> It would be nice to actually see the baseline.
> The question is whether it is roughly zero mean or not. For the covariance
> noise model to be appropriate the data should be zero mean, as is typically
> roughly the case after baseline correction or filtering with a high pass.
> What is plausible here also depends to on your data. Using the noise
> covariance here will relate your data to the amplitude structure of what
> you declare noise. Plot this for example using an empty room noise cov.
> Overall, from the distance your plots look plausible to me.
>
> On Wed, 10 May 2017 at 06:03, Graham Flick <grahamflick00 at gmail.com>
> wrote:
>
>> Hi All,
>>
>> I have a set of MEG data collected in a sentence processing paradigm,
>> where the critical words occur 6-7 words into the sentence. I'd like to
>> look at source-level evoked responses to these words via minimum-norm
>> estimates, without applying baseline correction.
>>
>> In this scenario, should I still apply baseline correction to the
>> pre-stimulus intervals that I use to estimate the noise covariance? Note
>> that in this design, pre-stimulus is actually pre-sentence, meaning that
>> there is about 4 seconds of data between these windows and the onset of the
>> epochs that will be inverted to source space.
>>
>> In attempt to address this question, I've plotted whitened evoked
>> responses from the start of the sentence to the target words using
>> different methods of covariance estimation, with and without baseline
>> correction applied to the 100 ms windows from which I estimated the
>> covariance. I've attached an example from one subject, and the pattern
>> shown there is consistent across quite a few subjects in the sample.
>>
>> In general, it looks like if I apply baseline correction to the window
>> from which I estimate covariance, the global field power of the whitened
>> response never reaches 1, even in the window in which the covariance was
>> estimated. In contrast, the GFP in the whitened response without baseline
>> correction looks more like what I'd expect to see. This pattern seems
>> unusual to me, but does it imply that I should not be be applying baseline
>> correction here? Or are there other factors that should be considered?
>>
>> Thanks!
>>
>> Graham
>>
>>
>> Here is a sample of the code used to generate the whitened responses for
>> the empirical estimator with/without baseline correction:
>>
>> raw = mne.io.read_raw_fif(fname_raw, preload=True)
>> events = mne.read_events(fname_event)
>> picks = mne.pick_types(raw.info, meg=True, eeg=False,
>> eog=False,exclude=bads)
>> epochstargetFull = mne.Epochs(raw, events, event_id = event_id,
>> tmin=-4.4,tmax=1.2, decim=5,reject=dict(mag=2e-12)
>> ,baseline=None,picks=picks,on_missing='ignore')
>> evokedtargetFull = epochstargetFull.average()
>>
>> method = 'empirical'
>>
>> # covariance with baseline correction applied
>> epochscov = mne.Epochs(raw, events, event_id=event_id, tmin=-4.4,
>> tmax=-4.3, decim=5, reject=dict(mag=2e-12), baseline=(-4.4,-4.3),
>> picks=picks, on_missing='ignore')
>> cov = mne.compute_covariance(epochscov, tmin=-4.4, tmax=-4.3,
>> method=method')
>> tmp = evokedtargetFull.plot_white(cov, show=False)
>> tmp.savefig('topright_empirical_Baselined.png')
>> del(epochscov)
>> del(cov)
>> del(tmp)
>>
>> # covariance without baseline correction applied
>> epochscov = mne.Epochs(raw, events, event_id=event_AF, tmin=-4.4,
>> tmax=-4.3, decim=5, reject=dict(mag=2e-12), baseline=None, picks=picks,
>> on_missing='ignore')
>> cov = mne.compute_covariance(epochscov, tmin=-4.4, tmax=-4.3,
>> method=method)
>> tmp = evokedtargetFull.plot_white(cov, show=False)
>> tmp.savefig('topleft_empirical_NoBaseline.png')
>>
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