[Mne_analysis] lowpass filtering data in sensor or source space

[Matthew Panichello]

Hi Alex,

Thanks for letting me know. I specified --picknormcomp .

Thanks,

Matt


On 11/16/12 4:05 AM, Alexandre Gramfort wrote:
> hi matt,
>
> juste make sure you used pick_normal or fixed orientation so you don't
> filter the signal
> after taking an absolute value.
>
> Best,
> Alex
>
> On Thu, Nov 15, 2012 at 8:56 PM, Matthew Panichello
> <panichem at nmr.mgh.harvard.edu> wrote:
>> Hi Hari,
>>
>> Thanks for clarifying. I'll try both methods on one of the ROIs and see
>> how much of a practical difference the filtering stage makes.
>>
>> Thanks,
>>
>> Matt
>>
>>
>> On 11/15/12 2:35 PM, Hari Bharadwaj wrote:
>>> Hi Matt,
>>>      For a given inverse operator, it is the same if you do the filtering in sensor or source space... The thing you might want to consider is if you want to make an inverse operator that is more tailored to the noise covariance that you get with the specific filtering you want to do. In that sense it is probably better at least on paper to filter in sensor space first and then generate a new noise covariance and inverse operator.
>>>
>>> Regards,
>>> Hari
>>>
>>>
>>>
>>> Hari Bharadwaj
>>> PhD Candidate, Biomedical Engineering,
>>> Auditory Neuroscience Laboratory
>>> Boston University, Boston, MA 02215
>>>
>>> Martinos Center for Biomedical Imaging,
>>> Massachusetts General Hospital
>>> Charlestown, MA 02129
>>>
>>> hari at nmr.mgh.harvard.edu
>>> Ph: 734-883-5954
>>>
>>> On Nov 15, 2012, at 1:54 PM, Matthew Panichello <panichem at nmr.mgh.harvard.edu> wrote:
>>>
>>>> Thanks Sheraz! The function works great.
>>>>
>>>> Beyond computational cost, there's no reason to worry about filtering in
>>>> source space?
>>>>
>>>> Thanks,
>>>>
>>>> Matt
>>>>
>>>> On 11/15/12 1:31 PM, sheraz at nmr.mgh.harvard.edu wrote:
>>>>> Filtering in source space is very costly, number of sources >> number of
>>>>> sensors, best filter to use which granted zero phase shift in Matlab is
>>>>> eegfilt (attached).
>>>>>
>>>>> Sheraz
>>>>>
>>>>>> Hello,
>>>>>>
>>>>>> I have used mne_compute_raw_inverse to produce the inverse solution for
>>>>>> raw data, and loaded this data into matlab.
>>>>>>
>>>>>> However, I have inspected the data and would like to lowpass filter the
>>>>>> data further before extracting the statistics that I am interested in.
>>>>>>
>>>>>> It seems like my options are to either low pass filter the raw sensor
>>>>>> data further before computing the inverse solution, or to filter the
>>>>>> source data directly in matlab. Is there any methodological reason to
>>>>>> prefer one method over the other?
>>>>>>
>>>>>> If filtering the source data in matlab is preferred (or if both methods
>>>>>> are fine), could anyone recommend a particular filter type? I've looked
>>>>>> into documentation for filtering in matlab and it seems like there are a
>>>>>> tremendous amount of options.
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> --
>>>>>> Matthew Panichello
>>>>>> Research Coordinator, Bar Group
>>>>>> Massachusetts General Hospital
>>>>>> Phone: 617-726-9034
>>>>>>
>>>>>> _______________________________________________
>>>>>> Mne_analysis mailing list
>>>>>> Mne_analysis at nmr.mgh.harvard.edu
>>>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>>> --
>>>> Matthew Panichello
>>>> Research Coordinator, Bar Group
>>>> Massachusetts General Hospital
>>>> Phone: 617-726-9034
>>>>
>>>> _______________________________________________
>>>> Mne_analysis mailing list
>>>> Mne_analysis at nmr.mgh.harvard.edu
>>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>>>
>>>>
>>
>> --
>> Matthew Panichello
>> Research Coordinator, Bar Group
>> Massachusetts General Hospital
>> Phone: 617-726-9034
>>
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>>
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>


-- 
Matthew Panichello
Research Coordinator, Bar Group
Massachusetts General Hospital
Phone: 617-726-9034