[Mne_analysis] Filtering and ICA memory issues

[dgw]

One thing to add to Denis's suggestion about ECG events. I encourage you to use a sample of ~300 plus throughout the recording (i.e. Find the events then take every 10th or something). As the ECG components/SSPs can shift over time depending on participant movement.
Hth
D


Sent from my Phone

> On Nov 25, 2015, at 11:54, Denis-Alexander Engemann <denis.engemann at gmail.com> wrote:
> 
> Hi Mads,
> 
> it seems you don't use the decim parameter, do you?
> Two things that I see immediately:
> It should in fact save a lot. With 1000HZ you can decimate even more, for ECG/EOG your sampling frequency should not be lower than 50 or so.
> Second as you have 1 hour of data the ecg_epochs will be huge, assuming you find many events.
> Very often only a few are necessary to do the detection. Have you tried picking the 100-200 first events?
> 
> We'll have a closer look soon.
> Denis
> 
>> On Wed, Nov 25, 2015 at 3:47 PM, Mads Jensen <mje.mads at gmail.com> wrote:
>> Hi,
>> 
>> I use sklearn 0.17 (from anaconda). I have tried to the
>> "decim" param. I remember it as being "3" for data with 1000Hz sfreq. But it didn't help much.
>> 
>> I have attach a script to show how I used it.
>> 
>> cheers,
>> mads
>> 
>> 
>> 
>>> On 25/11/15 15:29, Denis-Alexander Engemann wrote:
>>> Hi Mads,
>>> 
>>> Which version of sklearn are you using?
>>> Do you use the decim parameter for ICA?
>>> How do axactly do you use ICA?
>>> 50GB of memory is unexpected, it would mean that you make up to 10
>>> copies of your data.
>>> 
>>> 
>>> On Wed, Nov 25, 2015 at 3:24 PM, Mads Jensen <mje.mads at gmail.com
>>> <mailto:mje.mads at gmail.com>> wrote:
>>> 
>>>     Hi all,
>>> 
>>>     I would like to hear what people do to filter and run ICA and if there
>>>     is any advise.
>>> 
>>>     We usually have around an hour of recording which gives ~4.5 to 5GB of
>>>     raw fiff files. First filtering and then running ICA in MNE-python
>>>     requires a lot of memory, sometimes as much as 50GB. So, I fairly often
>>>     get a memory error.
>>> 
>>>     I would prefer not to downsample at this stage in the process. So, I
>>>     kindly ask if anybody has any thoughts and/or practises to avoid very
>>>     heavy memory use.
>>> 
>>>     best wishes,
>>>     mads
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