[Mne_analysis] Time-frequency PSD with CWT (morlet wavelet) in a single trial

Denis-Alexander Engemann denis.engemann at gmail.com
Thu Apr 24 09:58:40 EDT 2014
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Hi Arnaud,

here's your example with my edits

https://gist.github.com/dengemann/11255522

Best,
Denis



On Thu, Apr 24, 2014 at 10:52 AM, Arnaud Ferre
<arnaud.ferre.pro at gmail.com>wrote:

> Ok, so I'm not far!
> In fact, I think that what I see as "residual band" is the correct thing,
> but not correctly align with the correct frequency in the y-axis. The high
> power band in the bottom could be an artefact in the edge.
> So, I'm interested by your edits.
> Thank
>
>
> 2014-04-23 18:17 GMT+02:00 Denis A. Engemann <denis.engemann at gmail.com>:
>
> I was playing a bit with you example and got reasonable results with a
>> clear 10 hz modulation using the  single trial tfr function.
>> I can share my edits later with you. Btw. why don't we meet at Neurospin
>> at some point for lunch or a coffee --- maybe things are easier to tackle
>> then.
>> For raw and epochs and evoked API take a look here:
>>
>>
>> http://martinos.org/mne/stable/auto_examples/plot_from_raw_to_epochs_to_evoked.html
>>
>>
>> Best,
>> Denis
>>
>> On Apr 23, 2014, at 4:58 PM, Arnaud Ferre <arnaud.ferre.pro at gmail.com>
>> wrote:
>>
>> Hi Denis,
>>
>> I have tested with single_trial_power() but I obtain exactly the same
>> results that my use with _time_frequency(). Here is my code:
>> https://gist.github.com/arnaudferre/11215335. I don’t understand what
>> the problem is. With a sinusoid, I must have a band of high power around
>> the fundamental frequency of the sinusoid, right?
>>
>>
>> For the GUI, I don’t find documentation on the use with Windows. But ok,
>> I must compile the sources with the makefile. It’s been a long time that I
>> didn’t play with C! But I will try (I believe that I must download the
>> software Make to begin). But in fact, I don’t know why I spoke of GUI,
>> sorry. I think that I thought at a possibility to play with data directly
>> with a GUI in the way that I want.
>>
>>
>> I’m not sure to understand what you name “Raw object”. Is it the name of
>> an existing object from MNE? But, with the knowledge of this object, I can
>> write a function which return this directly. My problem is that I’m not an
>> expert on the data and her format. But if it’s substantially a problem of
>> parsing, I can step in the discussion.
>>
>>
>> I read your link on the S-transform. Yes, it could be very interesting to
>> test my data with this method. But I must already test older results from a
>> study with Fourier Transform. In literature, the wavelet transform with
>> morlet wavelet seems to be adequate to my project (Phase-Amplitude coupling
>> then cross-frequency coupling). So, I want finish with this method to have
>> my first result. But thank you, I keep it in a corner for later.
>>
>> Best,
>> Arnaud
>>
>>
>> 2014-04-23 11:22 GMT+02:00 Denis-Alexander Engemann <
>> denis.engemann at gmail.com>:
>>
>>> Hi Arnaud,
>>>
>>>
>>> On Wed, Apr 23, 2014 at 10:54 AM, Arnaud Ferre <
>>> arnaud.ferre.pro at gmail.com> wrote:
>>>
>>>> Hi Denis,
>>>> Thanks for the fast reply.
>>>>
>>>> Yes I can share my test. Here:
>>>> https://gist.github.com/arnaudferre/11206945.
>>>>
>>>>
>>> Thanks, will have a look later.
>>>
>>>
>>>>  Ok, I will try to use single_trial_power() function now.
>>>>
>>>> For the GUI, unfortunately, I must develop a ‘biologists-friendly’
>>>> software. In consequence (and with others constraints), I must develop on
>>>> Windows and the GUI doesn’t work on Windows if I have correctly understood.
>>>>
>>>>
>>> Which GUI? The MNE C GUI indeed compile on Windows. However,
>>> `mne.gui.coregistration` in Python is supposed to work on Windows.
>>>
>>>
>>>
>>>>  For my file format, I have already develop my own functions to parse
>>>> the original data (my goal: adapt raw signal to can use just the useful
>>>> data). This format can be ‘CED Spike2 SMR files’ or an alternative in TXT
>>>> from Local Field Potential acquisition. So, I store the data of a single
>>>> trial in a dictionary, then in a pickle file (maybe not a good idea…).
>>>>
>>>
>>> I agree. I would strongly encourage you to write a custom constructor
>>> function that returns a Raw object from your data. We're happy assist you
>>> with that Also it would be a great test case for new tools we're about to
>>> develop that aim at making exactly this task easier. In fact I need to
>>> write a couple of functions very soon that allow me to read in data stored
>>> in Mat files. Sounds like that would follow the same logic.
>>>
>>>
>>>> I do this certainly with my personal way. For these reasons, I tried to
>>>> adapt the MNE-Python script to read my data. It seems not far yet! But I
>>>> don’t know if I can really add idea in your conversation due to these
>>>> specific data.
>>>>
>>>>
>>> I think you can. See above. Also we're happy to add support to
>>> additional EEG / electrophysiology formats.
>>>
>>> Anyway, it seems that MNE contains all the tools what we need here. It’s
>>>> very encouraging yeah. But, I think we need time to know to use these tools.
>>>>
>>>> Arnaud
>>>>
>>>>
>>> FYI something I'm working on at the moment that might also be of
>>> interest to you:
>>>
>>> https://github.com/mne-tools/mne-python/pull/1233
>>>
>>> Best,
>>> Denis
>>>
>>>
>>>>
>>>>
>>>> 2014-04-19 12:34 GMT+02:00 Denis-Alexander Engemann <
>>>> denis.engemann at gmail.com>:
>>>>
>>>> Hi Arno,
>>>>>
>>>>> let my reply inline,
>>>>>
>>>>> On Fri, Apr 18, 2014 at 6:55 PM, Arnaud Ferre <
>>>>> arnaud.ferre.pro at gmail.com> wrote:
>>>>>
>>>>>> Hi all,
>>>>>> I work on EEG data in behavioral field. I understand the basics of
>>>>>> signal processing and I’m not so bad in Python. But for a beginner like me,
>>>>>> MNE-Python is pretty huge. I was advised to use MNE knowing my constraints,
>>>>>> but I’m starting to think that it’s maybe too evolved for a non-expert
>>>>>> scientist in this field like me.
>>>>>>
>>>>>  I’m just trying to draw a time-frequency PSD from a single trial with
>>>>>> a CWT (with morlet wavelets). I thought to have found what functions used,
>>>>>> but in testing my script, I don’t obtain correct result.
>>>>>>
>>>>>
>>>>>> In fact, I tested with a simple sinusoid function with a frequency of
>>>>>> 10Hz on 1sec (1000points). I expected to see a lot of "red blobs" aligned
>>>>>> on the 10Hz in y-axis but not. I have a continuous and very thin band
>>>>>> around 1.5 Hz and a residual band around the 90Hz. I looked the wavelets
>>>>>> and it seems fine. What is strange is that I see the correct number of
>>>>>> blob, so I think it’s not so far of correct result. In first, I thought of
>>>>>> a problem in adjusting my display, but it seems not.
>>>>>>
>>>>>
>>>>> Could you share an example script, e.g. using https://gist.github.com/
>>>>> That would be very helpful.
>>>>>
>>>>>
>>>>>>
>>>>>> I use mainly the function _time_frequency(data, Ws), one of the
>>>>>> functions in tfr.py in mne.time_frequency where data is a 2D-array
>>>>>> containing only my signal in data[0].
>>>>>>
>>>>>> I read this link:
>>>>>> http://martinos.org/mne/stable/auto_examples/stats/plot_cluster_1samp_test_time_frequency.html,
>>>>>> but as I don’t use data with the standard format, it seemed to me
>>>>>> complicated. Moreover, I think that _time_frequency(data, Ws) is just
>>>>>> needed to do what I want. And yet, maybe that the specificities of the
>>>>>> function single_trial_power in tfr.py is the solution…
>>>>>>
>>>>>>
>>>>>>
>>>>> In fact `single_trial_power` would be the way to go. (
>>>>> http://martinos.org/mne/dev/generated/mne.time_frequency.single_trial_power.html#mne.time_frequency.single_trial_power
>>>>> )
>>>>> In general it's not recommended not use functions starting with
>>>>> underscores unless you know exactly what you do. Things are much easier
>>>>> once you use the top-level API. Is your file format that prevents you from
>>>>> doing this? What kind of EEG  data do you use? We're currently working on
>>>>> improving support for custom data:
>>>>>
>>>>> https://github.com/mne-tools/mne-python/issues/1229
>>>>>
>>>>> Please feel free to participate in the discussion on Github and tell
>>>>> us more about your use case.
>>>>>
>>>>>
>>>>>> Thanks in advance
>>>>>> Arnaud
>>>>>>
>>>>>>
>>>>> I hope we can encourage you to keep exploring the new terrain ;-)
>>>>>
>>>>> Denis
>>>>>
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