Mne_analysis Digest, Vol 150, Issue 44

✉️ Mailing List Archive (read-only)
1

External Email - Use Caution

Dear All,

The response from epochs.drop_log is [['NO_DATA']], yet i get the message
"1 bad epochs dropped"

Is there anyway to figure out why all my epochs are being dropped?

*source code below*
        channames = ['Cz','Fz','C3','C4','F3','F4','P7','P8']
        ch_types = ['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg']
        # Sampling rate of the Nautilus machine
        sfreq = 250 # Hz for eeg
       # Create the info structure needed by MNE
        info = mne.create_info(ch_names=channames, sfreq=sfreq,
ch_types=ch_types)
        raw = mne.io.RawArray(dataconv1, info)
        raw.set_annotations(None)
        raw.del_proj()
        picks =mne.pick_types(raw.info, meg=False, eeg=True, stim=True,
eog=False,exclude='bads')
        raw=raw.filter(0.1, 120, fir_design='firwin')
        raw=raw.notch_filter(np.arange(50, 120, 50), picks=picks,
filter_length='auto', phase='zero')
        evenfile=mne.make_fixed_length_events(raw, id=1, start=0,
stop=None, duration=4.0, first_samp=True, overlap=0.0)
        epochs = mne.Epochs(raw, evenfile, event_id=event_id, tmin=-0.2,
tmax=4, picks=picks, baseline=(-0.2, 0), event_repeated='drop',proj=False,
preload=True, reject_by_annotation=None)
        print(epochs.drop_log)
data = epochs.get_data()

A. Ighoyota ben
Junior Researcher HCI (PhD in-view)
Tallinn University, Estonia
School of digital Technologies.
mobile:+372582 <+372%205832%206393>78794
skype: ighoyota-ben

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Today's Topics:

   1. Re: epoch dropped problem (Alexandre Gramfort)
   2. Re: loading the subject from BRAINSTORM (Alexandre Gramfort)
   3. Re: Mne_analysis Digest, Vol 150, Issue 40 (balandongiv at gmail.com)

----------------------------------------------------------------------

Message: 1
Date: Thu, 23 Jul 2020 22:57:00 +0200
From: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Subject: Re: [Mne_analysis] epoch dropped problem
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID:
        <CADeotZoS51gPzBn+oLTxCnLTgtpGy=
1DyWrFEnOhfM8Ouuevjg at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

What do epochs.drop_log and epochs.plot_drop_log() give you?

Alex

------------------------------

Message: 2
Date: Thu, 23 Jul 2020 22:58:15 +0200
From: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Subject: Re: [Mne_analysis] loading the subject from BRAINSTORM
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID:
        <
CADeotZrjoB0oSOmbXpzESX-_39Dwmqr6VNiSobMqg_043nOD9w at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

hi,

no it's not that simple as Brainstorm decimates the cortex while MNE
subsamples the high resolution mesh.

Alex

>
> External Email - Use Caution
>
> Hi All,
>
> Can we load in mne the subject (inorder to use the cortex mesh) used in
Brainstorm,
>
> Cheers
> Abdallah
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis

------------------------------

Message: 3
Date: Fri, 24 Jul 2020 09:55:33 +0800
From: <balandongiv at gmail.com>
Subject: Re: [Mne_analysis] Mne_analysis Digest, Vol 150, Issue 40
To: <mne_analysis at nmr.mgh.harvard.edu>
Message-ID: <004e01d6615d$85632b50$902981f0$@gmail.com>
Content-Type: text/plain; charset="us-ascii"

        External Email - Use Caution

Dear Clemens, Larson, Phillip,

Thanks for the detail explanation, really appreciate it.

Just a suggestion, maybe part of the discussion can be incorporated
somewhere along with mne FAQ or equivalent. This might be helpful,
especially to those new in the field.

Rodney

From: mne_analysis-bounces at nmr.mgh.harvard.edu
<mne_analysis-bounces at nmr.mgh.harvard.edu> On Behalf Of
mne_analysis-request at nmr.mgh.harvard.edu
Sent: Thursday, 23 July, 2020 9:10 PM
To: mne_analysis at nmr.mgh.harvard.edu
Subject: Mne_analysis Digest, Vol 150, Issue 40

Send Mne_analysis mailing list submissions to
        mne_analysis at nmr.mgh.harvard.edu

To subscribe or unsubscribe via the World Wide Web, visit
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or, via email, send a message with subject or body 'help' to
        mne_analysis-request at nmr.mgh.harvard.edu

You can reach the person managing the list at
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When replying, please edit your Subject line so it is more specific than
"Re: Contents of Mne_analysis digest..."

Today's Topics:

   1. Re: Why does MNE resample method does not sample the data
      point to point? (Eric Larson)
   2. Re: Why does MNE resample method does not sample the data
      point to point? (Brunner, Clemens (clemens.brunner at uni-graz.at))
   3. Re: Why does MNE resample method does not sample the data
      point to point? (Phillip Alday)

----------------------------------------------------------------------

Message: 1
Date: Thu, 23 Jul 2020 08:40:56 -0400
From: Eric Larson <larson.eric.d at gmail.com>
Subject: Re: [Mne_analysis] Why does MNE resample method does not
        sample the data point to point?
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID:
        <
CAGu2niVV+79nq5Yu17B4VzB3PS71x7sFTy8HKNukDxnaDdi6hQ at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

>
> My understanding of downsampling is that it is an operation to
> decrease the sample rate of x by keeping the first sample and then
> every nth sample after the first.
>

Resampling typically consists of two steps: low-pass filtering to avoid
aliasing, then sample rate reduction (subselecting samples from the
resulting signal). The low-passing actually changes the values, so the
subselection-of-filtered-data step will not necessarily yield points that
were "on" the original signal.

> May I know whether this issue is due to the ringing artifacts or due
> to other problems?
>

In this case it's likely due to the (implicit) low-pass filtering in the
frequency-domain resampling of the signal. It looks pretty reasonable to
me.
If you want to play around with it a bit, you can

1. Call scipy.signal.resample directly on your data and see how closely it
matches.
2. Pad your signal, call scipy.signal.resample, and remove the (now
reduced-length) padding -- this is what MNE does internally.
3. Use scipy.signal.resample_poly directly on your data.
4. Manually low-pass filter and then directly subselect samples from the
low-passed signal, which is what resample_poly does internally.

Hopefully these all give similar results for your signal(s).

Eric
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------------------------------

Message: 2
Date: Thu, 23 Jul 2020 12:57:29 +0000
From: "Brunner, Clemens (clemens.brunner at uni-graz.at)"
        <clemens.brunner at uni-graz.at>
Subject: Re: [Mne_analysis] Why does MNE resample method does not
        sample the data point to point?
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID: <21199B34-7BD6-4C1B-81CC-749DFB86E3FC at uni-graz.at>
Content-Type: text/plain; charset="us-ascii"

        External Email - Use Caution

Also note that the resample example
(
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.
html
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html&gt;\)
shows upsampling, i.e. the data has a lower sampling rate than the
resampled result. However, in the case of downsampling it is usually
necessary to avoid aliasing of frequencies above the resampled Nyquist
frequency. Therefore, the signal is typically low-pass filtered before the
resampling step. As Eric mentioned, this anti-aliasing filter is what
actually changes the signal values, but it is necessary to avoid aliasing
artifacts.

AFAIK, scipy.signal.resample doesn't include an anti-aliasing filter, but
both scipy.signal.resample_poly as well as scipy.signal.decimate apply such
a low-pass filter before resampling. That's also what MNE does.

Clemens

>
> External Email - Use Caution
>
>
> My understanding of downsampling is that it is an operation to decrease
the sample rate of x by keeping the first sample and then every nth sample
after the first.
>
> Resampling typically consists of two steps: low-pass filtering to avoid
aliasing, then sample rate reduction (subselecting samples from the
resulting signal). The low-passing actually changes the values, so the
subselection-of-filtered-data step will not necessarily yield points that
were "on" the original signal.
>
> May I know whether this issue is due to the ringing artifacts or due to
other problems?
>
> In this case it's likely due to the (implicit) low-pass filtering in
> the frequency-domain resampling of the signal. It looks pretty
> reasonable to me. If you want to play around with it a bit, you can
>
> 1. Call scipy.signal.resample directly on your data and see how closely
it
matches.
> 2. Pad your signal, call scipy.signal.resample, and remove the (now
reduced-length) padding -- this is what MNE does internally.
> 3. Use scipy.signal.resample_poly directly on your data.
> 4. Manually low-pass filter and then directly subselect samples from the
low-passed signal, which is what resample_poly does internally.
>
> Hopefully these all give similar results for your signal(s).
>
> Eric
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis

------------------------------

Message: 3
Date: Thu, 23 Jul 2020 15:09:44 +0200
From: Phillip Alday <phillip.alday at mpi.nl>
Subject: Re: [Mne_analysis] Why does MNE resample method does not
        sample the data point to point?
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>, "Brunner, Clemens
        (clemens.brunner at uni-graz.at)" <clemens.brunner at uni-graz.at>
Message-ID: <16e39842-34bd-465d-9491-b5651302add4 at mpi.nl>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

I think the up- vs. downsampling distinction is also really important for
expectations here, as is the distinction between decimating and resampling
(I recall there was a thread about that a few years back with similar
confusion, if somebody wants to do the effort of searching for it)

Phillip

> External Email - Use Caution
>
> Also note that the resample example
(
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.
html
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html&gt;\)
shows upsampling, i.e. the data has a lower sampling rate than the
resampled result. However, in the case of downsampling it is usually
necessary to avoid aliasing of frequencies above the resampled Nyquist
frequency. Therefore, the signal is typically low-pass filtered before the
resampling step. As Eric mentioned, this anti-aliasing filter is what
actually changes the signal values, but it is necessary to avoid aliasing
artifacts.
>
> AFAIK, scipy.signal.resample doesn't include an anti-aliasing filter, but
both scipy.signal.resample_poly as well as scipy.signal.decimate apply such
a low-pass filter before resampling. That's also what MNE does.
>
> Clemens
>
>
>>
>> External Email - Use Caution
>>
>>
>> My understanding of downsampling is that it is an operation to decrease
the sample rate of x by keeping the first sample and then every nth sample
after the first.
>>
>> Resampling typically consists of two steps: low-pass filtering to avoid
aliasing, then sample rate reduction (subselecting samples from the
resulting signal). The low-passing actually changes the values, so the
subselection-of-filtered-data step will not necessarily yield points that
were "on" the original signal.
>>
>> May I know whether this issue is due to the ringing artifacts or due to
other problems?
>>
>> In this case it's likely due to the (implicit) low-pass filtering in
>> the frequency-domain resampling of the signal. It looks pretty
>> reasonable to me. If you want to play around with it a bit, you can
>>
>> 1. Call scipy.signal.resample directly on your data and see how closely
it matches.
>> 2. Pad your signal, call scipy.signal.resample, and remove the (now
reduced-length) padding -- this is what MNE does internally.
>> 3. Use scipy.signal.resample_poly directly on your data.
>> 4. Manually low-pass filter and then directly subselect samples from the
low-passed signal, which is what resample_poly does internally.
>>
>> Hopefully these all give similar results for your signal(s).
>>
>> Eric
>>
>> _______________________________________________
>> Mne_analysis mailing list
>> Mne_analysis at nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis

------------------------------

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Mne_analysis mailing list
Mne_analysis at nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis

End of Mne_analysis Digest, Vol 150, Issue 40
*********************************************

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2

External Email - Use Caution

Dear All,

The response from epochs.drop_log is [['NO_DATA']], yet i get the message
"1 bad epochs dropped"

Is there anyway to figure out why all my epochs are being dropped?

*source code below*
        channames = ['Cz','Fz','C3','C4','F3','F4','P7','P8']
        ch_types = ['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg']
        # Sampling rate of the Nautilus machine
        sfreq = 250 # Hz for eeg
       # Create the info structure needed by MNE
        info = mne.create_info(ch_names=channames, sfreq=sfreq,
ch_types=ch_types)
        raw = mne.io.RawArray(dataconv1, info)
        raw.set_annotations(None)
        raw.del_proj()
        picks =mne.pick_types(raw.info, meg=False, eeg=True, stim=True,
eog=False,exclude='bads')
        raw=raw.filter(0.1, 120, fir_design='firwin')
        raw=raw.notch_filter(np.arange(50, 120, 50), picks=picks,
filter_length='auto', phase='zero')
        evenfile=mne.make_fixed_length_events(raw, id=1, start=0,
stop=None, duration=4.0, first_samp=True, overlap=0.0)
        epochs = mne.Epochs(raw, evenfile, event_id=event_id, tmin=-0.2,
tmax=4, picks=picks, baseline=(-0.2, 0), event_repeated='drop',proj=False,
preload=True, reject_by_annotation=None)
        print(epochs.drop_log)
data = epochs.get_data()

A. Ighoyota ben
Junior Researcher HCI (PhD in-view)
Tallinn University, Estonia
School of digital Technologies.
mobile:+372582 <+372%205832%206393>78794
skype: ighoyota-ben

Send Mne_analysis mailing list submissions to
        mne_analysis at nmr.mgh.harvard.edu

To subscribe or unsubscribe via the World Wide Web, visit
        Mne_analysis Info Page
or, via email, send a message with subject or body 'help' to
        mne_analysis-request at nmr.mgh.harvard.edu

You can reach the person managing the list at
        mne_analysis-owner at nmr.mgh.harvard.edu

When replying, please edit your Subject line so it is more specific
than "Re: Contents of Mne_analysis digest..."

Today's Topics:

   1. Re: epoch dropped problem (Alexandre Gramfort)
   2. Re: loading the subject from BRAINSTORM (Alexandre Gramfort)
   3. Re: Mne_analysis Digest, Vol 150, Issue 40 (balandongiv at gmail.com)

----------------------------------------------------------------------

Message: 1
Date: Thu, 23 Jul 2020 22:57:00 +0200
From: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Subject: Re: [Mne_analysis] epoch dropped problem
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID:
        <CADeotZoS51gPzBn+oLTxCnLTgtpGy=
1DyWrFEnOhfM8Ouuevjg at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

What do epochs.drop_log and epochs.plot_drop_log() give you?

Alex

------------------------------

Message: 2
Date: Thu, 23 Jul 2020 22:58:15 +0200
From: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Subject: Re: [Mne_analysis] loading the subject from BRAINSTORM
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID:
        <
CADeotZrjoB0oSOmbXpzESX-_39Dwmqr6VNiSobMqg_043nOD9w at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

hi,

no it's not that simple as Brainstorm decimates the cortex while MNE
subsamples the high resolution mesh.

Alex

>
> External Email - Use Caution
>
> Hi All,
>
> Can we load in mne the subject (inorder to use the cortex mesh) used in
Brainstorm,
>
> Cheers
> Abdallah
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> Mne_analysis Info Page

------------------------------

Message: 3
Date: Fri, 24 Jul 2020 09:55:33 +0800
From: <balandongiv at gmail.com>
Subject: Re: [Mne_analysis] Mne_analysis Digest, Vol 150, Issue 40
To: <mne_analysis at nmr.mgh.harvard.edu>
Message-ID: <004e01d6615d$85632b50$902981f0$@gmail.com>
Content-Type: text/plain; charset="us-ascii"

        External Email - Use Caution

Dear Clemens, Larson, Phillip,

Thanks for the detail explanation, really appreciate it.

Just a suggestion, maybe part of the discussion can be incorporated
somewhere along with mne FAQ or equivalent. This might be helpful,
especially to those new in the field.

Rodney

From: mne_analysis-bounces at nmr.mgh.harvard.edu
<mne_analysis-bounces at nmr.mgh.harvard.edu> On Behalf Of
mne_analysis-request at nmr.mgh.harvard.edu
Sent: Thursday, 23 July, 2020 9:10 PM
To: mne_analysis at nmr.mgh.harvard.edu
Subject: Mne_analysis Digest, Vol 150, Issue 40

Send Mne_analysis mailing list submissions to
        mne_analysis at nmr.mgh.harvard.edu

To subscribe or unsubscribe via the World Wide Web, visit
        Mne_analysis Info Page
or, via email, send a message with subject or body 'help' to
        mne_analysis-request at nmr.mgh.harvard.edu

You can reach the person managing the list at
        mne_analysis-owner at nmr.mgh.harvard.edu

When replying, please edit your Subject line so it is more specific than
"Re: Contents of Mne_analysis digest..."

Today's Topics:

   1. Re: Why does MNE resample method does not sample the data
      point to point? (Eric Larson)
   2. Re: Why does MNE resample method does not sample the data
      point to point? (Brunner, Clemens (clemens.brunner at uni-graz.at))
   3. Re: Why does MNE resample method does not sample the data
      point to point? (Phillip Alday)

----------------------------------------------------------------------

Message: 1
Date: Thu, 23 Jul 2020 08:40:56 -0400
From: Eric Larson <larson.eric.d at gmail.com>
Subject: Re: [Mne_analysis] Why does MNE resample method does not
        sample the data point to point?
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID:
        <
CAGu2niVV+79nq5Yu17B4VzB3PS71x7sFTy8HKNukDxnaDdi6hQ at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

>
> My understanding of downsampling is that it is an operation to
> decrease the sample rate of x by keeping the first sample and then
> every nth sample after the first.
>

Resampling typically consists of two steps: low-pass filtering to avoid
aliasing, then sample rate reduction (subselecting samples from the
resulting signal). The low-passing actually changes the values, so the
subselection-of-filtered-data step will not necessarily yield points that
were "on" the original signal.

> May I know whether this issue is due to the ringing artifacts or due
> to other problems?
>

In this case it's likely due to the (implicit) low-pass filtering in the
frequency-domain resampling of the signal. It looks pretty reasonable to
me.
If you want to play around with it a bit, you can

1. Call scipy.signal.resample directly on your data and see how closely it
matches.
2. Pad your signal, call scipy.signal.resample, and remove the (now
reduced-length) padding -- this is what MNE does internally.
3. Use scipy.signal.resample_poly directly on your data.
4. Manually low-pass filter and then directly subselect samples from the
low-passed signal, which is what resample_poly does internally.

Hopefully these all give similar results for your signal(s).

Eric
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------------------------------

Message: 2
Date: Thu, 23 Jul 2020 12:57:29 +0000
From: "Brunner, Clemens (clemens.brunner at uni-graz.at)"
        <clemens.brunner at uni-graz.at>
Subject: Re: [Mne_analysis] Why does MNE resample method does not
        sample the data point to point?
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>
Message-ID: <21199B34-7BD6-4C1B-81CC-749DFB86E3FC at uni-graz.at>
Content-Type: text/plain; charset="us-ascii"

        External Email - Use Caution

Also note that the resample example
(
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.
html
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html&gt;\)
shows upsampling, i.e. the data has a lower sampling rate than the
resampled result. However, in the case of downsampling it is usually
necessary to avoid aliasing of frequencies above the resampled Nyquist
frequency. Therefore, the signal is typically low-pass filtered before the
resampling step. As Eric mentioned, this anti-aliasing filter is what
actually changes the signal values, but it is necessary to avoid aliasing
artifacts.

AFAIK, scipy.signal.resample doesn't include an anti-aliasing filter, but
both scipy.signal.resample_poly as well as scipy.signal.decimate apply such
a low-pass filter before resampling. That's also what MNE does.

Clemens

>
> External Email - Use Caution
>
>
> My understanding of downsampling is that it is an operation to decrease
the sample rate of x by keeping the first sample and then every nth sample
after the first.
>
> Resampling typically consists of two steps: low-pass filtering to avoid
aliasing, then sample rate reduction (subselecting samples from the
resulting signal). The low-passing actually changes the values, so the
subselection-of-filtered-data step will not necessarily yield points that
were "on" the original signal.
>
> May I know whether this issue is due to the ringing artifacts or due to
other problems?
>
> In this case it's likely due to the (implicit) low-pass filtering in
> the frequency-domain resampling of the signal. It looks pretty
> reasonable to me. If you want to play around with it a bit, you can
>
> 1. Call scipy.signal.resample directly on your data and see how closely
it
matches.
> 2. Pad your signal, call scipy.signal.resample, and remove the (now
reduced-length) padding -- this is what MNE does internally.
> 3. Use scipy.signal.resample_poly directly on your data.
> 4. Manually low-pass filter and then directly subselect samples from the
low-passed signal, which is what resample_poly does internally.
>
> Hopefully these all give similar results for your signal(s).
>
> Eric
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> Mne_analysis Info Page

------------------------------

Message: 3
Date: Thu, 23 Jul 2020 15:09:44 +0200
From: Phillip Alday <phillip.alday at mpi.nl>
Subject: Re: [Mne_analysis] Why does MNE resample method does not
        sample the data point to point?
To: Discussion and support forum for the users of MNE Software
        <mne_analysis at nmr.mgh.harvard.edu>, "Brunner, Clemens
        (clemens.brunner at uni-graz.at)" <clemens.brunner at uni-graz.at>
Message-ID: <16e39842-34bd-465d-9491-b5651302add4 at mpi.nl>
Content-Type: text/plain; charset="utf-8"

        External Email - Use Caution

I think the up- vs. downsampling distinction is also really important for
expectations here, as is the distinction between decimating and resampling
(I recall there was a thread about that a few years back with similar
confusion, if somebody wants to do the effort of searching for it)

Phillip

> External Email - Use Caution
>
> Also note that the resample example
(
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.
html
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html&gt;\)
shows upsampling, i.e. the data has a lower sampling rate than the
resampled result. However, in the case of downsampling it is usually
necessary to avoid aliasing of frequencies above the resampled Nyquist
frequency. Therefore, the signal is typically low-pass filtered before the
resampling step. As Eric mentioned, this anti-aliasing filter is what
actually changes the signal values, but it is necessary to avoid aliasing
artifacts.
>
> AFAIK, scipy.signal.resample doesn't include an anti-aliasing filter, but
both scipy.signal.resample_poly as well as scipy.signal.decimate apply such
a low-pass filter before resampling. That's also what MNE does.
>
> Clemens
>
>
>>
>> External Email - Use Caution
>>
>>
>> My understanding of downsampling is that it is an operation to decrease
the sample rate of x by keeping the first sample and then every nth sample
after the first.
>>
>> Resampling typically consists of two steps: low-pass filtering to avoid
aliasing, then sample rate reduction (subselecting samples from the
resulting signal). The low-passing actually changes the values, so the
subselection-of-filtered-data step will not necessarily yield points that
were "on" the original signal.
>>
>> May I know whether this issue is due to the ringing artifacts or due to
other problems?
>>
>> In this case it's likely due to the (implicit) low-pass filtering in
>> the frequency-domain resampling of the signal. It looks pretty
>> reasonable to me. If you want to play around with it a bit, you can
>>
>> 1. Call scipy.signal.resample directly on your data and see how closely
it matches.
>> 2. Pad your signal, call scipy.signal.resample, and remove the (now
reduced-length) padding -- this is what MNE does internally.
>> 3. Use scipy.signal.resample_poly directly on your data.
>> 4. Manually low-pass filter and then directly subselect samples from the
low-passed signal, which is what resample_poly does internally.
>>
>> Hopefully these all give similar results for your signal(s).
>>
>> Eric
>>
>> _______________________________________________
>> Mne_analysis mailing list
>> Mne_analysis at nmr.mgh.harvard.edu
>> Mne_analysis Info Page
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> Mne_analysis Info Page

------------------------------

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3

External Email - Use Caution

It's hard to know for sure why Epochs are getting dropped because your example code doesn't include the definition of `dataconv1` (nor `event_id`). However, with your given code you are guaranteed to have the first epoch get dropped, because your call to `make_fixed_length_events` starts at zero, but you're trying to make epochs with `tmin=-0.2`. If your first event is at time zero, there isn't any data before that, so the first epoch will always get dropped with error "NO_DATA". If you change the `start` parameter of `make_fixed_length_events` to be >0.2, that won't happen.

Here is a (simplified) version of your code that uses fake data (random numbers); when I run this I see the first epoch dropped because "NO_DATA" and the last epoch dropped because "TOO_SHORT", but the other 8 epochs in the middle are retained. I skipped defining `picks` (your example was picking all channels, which is the default anyway) and I skipped filtering because I'm using fake data so there's no way to know from my code whether filtering is influencing the epoch dropping of your real data.

import numpy as np
import mne
rng = np.random.default_rng()
channames = ['Cz','Fz','C3','C4','F3','F4','P7','P8']
ch_types = ['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg']
sfreq = 250 # Hz for eeg
# fake data
dataconv1 = rng.normal(size=(len(channames), 40 * sfreq)) # 40 seconds of data
# Create the info structure needed by MNE
info = mne.create_info(ch_names=channames, sfreq=sfreq, ch_types=ch_types)
raw = mne.io.RawArray(dataconv1, info)
evenfile = mne.make_fixed_length_events(raw, id=1, start=0, stop=None,
duration=4.0, first_samp=True,
overlap=0.0)
event_id = dict(foo=1)
epochs = mne.Epochs(raw, evenfile, event_id=event_id, tmin=-0.2, tmax=4,
baseline=(-0.2, 0), event_repeated='drop',
proj=False, preload=True, reject_by_annotation=None)
print(epochs.drop_log)

-- dan
Daniel McCloy
https://dan.mccloy.info
Research Scientist
Institute for Learning and Brain Sciences
University of Washington

??? Original Message ???

4

External Email - Use Caution

how long is your raw file?

what does evenfile contain?

I suspect your raw file is too short

A