[BUG] Correct annotation onset for exportation to EDF and EEGLAB by qian-chu · Pull Request #12656 · mne-tools/mne-python

qian-chu · 2024-06-10T20:00:00Z

When cropping the start of a recording, raw.first_time is updated while annotations.onset is conveniently untouched. However, when exporting to another format where times are reset (starting from zero), annotations.onset should be corrected so that they represent relative time from the first sample.

This correction has been performed when fmt=‘brainvision’:

mne-python/mne/export/_brainvision.py

Lines 78 to 85 in e2c8010

    
           if events is not None: 
        
               # subtract raw.first_samp because brainvision marks events starting from the 
        
               # first available data point and ignores the raw.first_samp 
        
               assert isinstance(events, np.ndarray), msg 
        
               assert events.ndim == 2, msg 
        
               assert events.shape[-1] == 3, msg 
        
               events[:, 0] -= raw.first_samp 
        
               events = events[:, [0, 2]]  # reorder for pybv required order

But is curiously missing when fmt=‘edf’ or fmt=‘eeglab’:

mne-python/mne/export/_edf.py

Lines 200 to 213 in e2c8010

    
           annotations = [] 
        
           for desc, onset, duration, ch_names in zip( 
        
               raw.annotations.description, 
        
               raw.annotations.onset, 
        
               raw.annotations.duration, 
        
               raw.annotations.ch_names, 
        
           ): 
        
               if ch_names: 
        
                   for ch_name in ch_names: 
        
                       annotations.append( 
        
                           EdfAnnotation(onset, duration, desc + f"@@{ch_name}") 
        
                       ) 
        
               else: 
        
                   annotations.append(EdfAnnotation(onset, duration, desc))

mne-python/mne/export/_eeglab.py

Lines 28 to 32 in e2c8010

    
           annotations = [ 
        
               raw.annotations.description, 
        
               raw.annotations.onset, 
        
               raw.annotations.duration, 
        
           ]

This PR aims to fix this by performing the similar correction (annotations.onset - raw.first_time

qian-chu · 2024-06-10T20:04:47Z

mne/export/tests/test_export.py

+@pytest.mark.parametrize("tmin", (0, 1, 5, 10))
+def test_export_raw_eeglab_annotations(tmp_path, tmin):
+    """Test that exporting EEGLAB preserves annotations and corects for raw.first_time."""
+    pytest.importorskip("eeglabio")
+    raw = read_raw_fif(fname_raw, preload=True)
+    raw.apply_proj()
+    annotations = Annotations(
+        onset=[0.01, 0.05, 0.90, 1.05],
+        duration=[0, 1, 0, 0],
+        description=["test1", "test2", "test3", "test4"],
+        ch_names=[["MEG 0113"], ["MEG 0113", "MEG 0132"], [], ["MEG 0143"]],
+    )
+    raw.set_annotations(annotations)
+    raw.crop(tmin)
+
+    # export
+    temp_fname = tmp_path / "test.set"
+    raw.export(temp_fname)
+
+    # read in the file
+    with pytest.warns(RuntimeWarning, match="is above the 99th percentile"):
+        raw_read = read_raw_eeglab(temp_fname, preload=True, montage_units="m")
+    assert raw_read.first_time == 0
+
+    valid_annot = raw.annotations.onset >= tmin
+    assert_array_almost_equal(
+        raw.annotations.onset[valid_annot] - raw.first_time,
+        raw_read.annotations.onset - raw_read.first_time,
+    )
+    assert_array_equal(
+        raw.annotations.duration[valid_annot], raw_read.annotations.duration
+    )
+    assert_array_equal(
+        raw.annotations.description[valid_annot], raw_read.annotations.description
+    )


Actually should this annotation test for EEGLAB been written before, the bug should be noticable because the test Raw has actually been cropped. The saved onset would not correspond to the original onset.

cbrnr · 2024-06-11T05:05:26Z

mne/export/tests/test_export.py

+    if tmin % 1 == 0:
+        expectation = nullcontext()
+    else:
+        expectation = pytest.warns(
+            RuntimeWarning, match="EDF format requires equal-length data blocks"
+        )


What is this check doing? If you are checking for tmin to be an integer, you could also use tmin.is_integer(), but is this what is required to have "equal-length data blocks"?

Because the constructed raw signal is 2 sec long and edfio can segment it into 2 data records of 1 sec. If a non-integer amount of time is cropped, then the signal is no longer a multiple of 1 sec and edfio will append zeroes and issue a RuntimeWarning. Maybe this should have been a test on its own but I'm adding it here since pytest wouldn't pass me otherwise.

As for the %1 == 0 condition, I was thinking to make space for more flexible use should I know how edfio determines data record length. For example if one can specify a data record length of .5 or 2 s, then the statement can be replaced with %data_length == 0. But I agree it looks uncessary in its current form.

cbrnr · 2024-06-11T05:07:35Z

When cropping the start of a recording, raw.first_time is corrected while annotations.onset is conveniently untouched.

Hmmm, really? Annotations are not affected by cropping? Why would that be convenient?

qian-chu · 2024-06-11T07:47:02Z

When cropping the start of a recording, raw.first_time is corrected while annotations.onset is conveniently untouched.

Hmmm, really? Annotations are not affected by cropping? Why would that be convenient?

At least when annotations.orig_time is not None:

mne-python/mne/io/base.py

Lines 1560 to 1570 in e2c8010

    
           annotations = self.annotations 
        
           # now call setter to filter out annotations outside of interval 
        
           if annotations.orig_time is None: 
        
               assert self.info["meas_date"] is None 
        
               # When self.info['meas_date'] is None (which is guaranteed if 
        
               # self.annotations.orig_time is None), when we do the 
        
               # self.set_annotations, it's assumed that the annotations onset 
        
               # are relative to first_time, so we have to subtract it, then 
        
               # set_annotations will put it back. 
        
               annotations.onset -= self.first_time 
        
           self.set_annotations(annotations, False)

So that when annotations have their own time reference, cropping the data wouldn't affect them.

Actually this is a good reminder that we might need to account for different annotations.orig_time. The onset correction should have been corrected when annotations.orig_time is None. Will do a fix later.

cbrnr · 2024-06-11T10:56:45Z

To be honest, I didn't even know that annotations work like that. I always thought that annotations.onset are onsets in seconds relative to the start of the data. So I expected that cropping should shift the onsets in general, not just for export. If that was the case, we would not have to deal with correcting onsets on export in the first place.

qian-chu · 2024-06-11T12:18:37Z

To be honest, I didn't even know that annotations work like that. I always thought that annotations.onset are onsets in seconds relative to the start of the data. So I expected that cropping should shift the onsets in general, not just for export. If that was the case, we would not have to deal with correcting onsets on export in the first place.

I'm not too sure how that will work out. Do you mean that cropping should always reset annotations.onset and then force annotations.orig_time=None?

----------- meas_date=XX, orig_time=YY -----------------------------

     |              +------------------+
     |______________|     RAW          |
     |              |                  |
     |              +------------------+
 meas_date      first_samp
     .
     .         |         +------+
     .         |_________| ANOT |
     .         |         |      |
     .         |         +------+
     .     orig_time   onset[0]
     .
     |                   +------+
     |___________________|      |
     |                   |      |
     |                   +------+
 orig_time            onset[0]'

----------- meas_date=XX, orig_time=None ---------------------------

     |              +------------------+
     |______________|     RAW          |
     |              |                  |
     |              +------------------+
     .              N         +------+
     .              o_________| ANOT |
     .              n         |      |
     .              e         +------+
     .
     |                        +------+
     |________________________|      |
     |                        |      |
     |                        +------+
 orig_time                 onset[0]'

----------- meas_date=None, orig_time=YY ---------------------------

     N              +------------------+
     o______________|     RAW          |
     n              |                  |
     e              +------------------+
               |         +------+
               |_________| ANOT |
               |         |      |
               |         +------+

            [[[ CRASH ]]]

----------- meas_date=None, orig_time=None -------------------------

     N              +------------------+
     o______________|     RAW          |
     n              |                  |
     e              +------------------+
     .              N         +------+
     .              o_________| ANOT |
     .              n         |      |
     .              e         +------+
     .
     N                        +------+
     o________________________|      |
     n                        |      |
     e                        +------+
 orig_time                 onset[0]'

cbrnr · 2024-07-03T14:45:52Z

Maybe it's just me, but I gave up trying to understand how this works. The ASCII diagram is probably meant to be helpful, but for me it is the complete opposite, I have no idea how these different concepts (meas_date, orig_time, first_samp, and whatnot) actually work, sorry.

hoechenberger · 2024-07-03T14:48:33Z

I agree, I've tried several times over the past couple of years to decipher what it's trying to tell me and at one point just gave up. It's just been trial and error for me regarding all things annotations ever since 😅

qian-chu · 2024-07-03T14:54:38Z

Maybe it's just me, but I gave up trying to understand how this works. The ASCII diagram is probably meant to be helpful, but for me it is the complete opposite, I have no idea how these different concepts (meas_date, orig_time, first_samp, and whatnot) actually work, sorry.

It's definitely OK! As I'm re-looking at this PR after some time I'm also struggling to wrap my head around this system. FYI this diagram was copied from https://mne.tools/dev/generated/mne.Annotations.html.

One potential conflict I found is, the diagram says when meas_date=None, orig_time=YY it should result in error, yet in crop() it asserts the following:

mne-python/mne/io/base.py

Lines 1562 to 1563 in 4954672

    
           if annotations.orig_time is None: 
        
               assert self.info["meas_date"] is None

instead of

if self.info["meas_date"] is None:
     assert annotations.orig_time is None

If someone who's familiar with the design can clarify that would be great. But I do confirm that the EDF and EEGLAB export will malfunction without correcting for first_time so eventually we would want this fix.

qian-chu · 2024-12-13T14:57:39Z

Coming back to this issue after some time, I re-confirmed the existence of the problem with a minimalist code:

import numpy as np
from mne import create_info, Annotations
from mne.io import RawArray, read_raw_brainvision, read_raw_edf, read_raw_eeglab
from mne.viz import set_browser_backend

set_browser_backend('qt')

# Create a raw object of SR 1000 Hz, all zero, except for 1s of 1e-6 from 2-3s
data = np.zeros((1, 5000))
data[0, 2000:3000] = 1
scalings = dict(eeg=1)

info = create_info(['CH1'], 1000, ['eeg'])
raw_orig = RawArray(data, info)

annot = Annotations(onset=[2], duration=[1], description=['stim'])
raw_orig.set_annotations(annot)

# Crop raw to 1-5s
raw_orig.crop(1)
fig_orig = raw_orig.plot(scalings=scalings)
fig_orig.grab().save('orig.png')

# Export to BrainVision and re-read
raw_orig.export('test.vhdr')
raw_brainvision = read_raw_brainvision('test.vhdr')
fig_brainvision = raw_brainvision.plot(scalings=scalings)
fig_brainvision.grab().save('brainvision.png')

# Export to EDF and re-read
raw_orig.export('test.edf')
raw_edf = read_raw_edf('test.edf')
fig_edf = raw_edf.plot(scalings=scalings)
fig_edf.grab().save('edf.png')

# Export to EEGLAB and re-read
raw_orig.export('test.set')
raw_eeglab = read_raw_eeglab('test.set')
fig_eeglab = raw_eeglab.plot(scalings=scalings)
fig_eeglab.grab().save('eeglab.png')

Outputs using the current `main` of MNE

Original raw array

BrainVision (functions properly)

EDF

EEGLAB

Outputs using the PR branch

Original raw array

BrainVision

EDF

EEGLAB

cbrnr · 2024-12-13T15:52:32Z

Very nice, thanks for this example, this makes the issue really easy to see! Could you take a look at the failing tests?

mne/export/_edf.py

cbrnr · 2025-01-03T09:37:30Z

This looks good to me, just one last comment: do you think it makes sense to update the export code for BrainVision so that all formats consistently use _sync_onset? I think it would be great to be as consistent as possible.

For merging, I'd appreciate if @larsoner and/or @drammock could have a final look.

qian-chu · 2025-01-03T15:09:09Z

This looks good to me, just one last comment: do you think it makes sense to update the export code for BrainVision so that all formats consistently use _sync_onset? I think it would be great to be as consistent as possible.

For merging, I'd appreciate if @larsoner and/or @drammock could have a final look.

It might be a bit tricky as _mne_annots2pybv_events() loops over annotations instead of taking the numpy arrays individually:

mne-python/mne/export/_brainvision.py

Lines 108 to 116 in fd8c1ee

    
           def _mne_annots2pybv_events(raw): 
        
               """Convert mne Annotations to pybv events.""" 
        
               events = [] 
        
               for annot in raw.annotations: 
        
                   # handle onset and duration: seconds to sample, relative to 
        
                   # raw.first_samp / raw.first_time 
        
                   onset = annot["onset"] - raw.first_time 
        
                   onset = raw.time_as_index(onset).astype(int)[0] 
        
                   duration = int(annot["duration"] * raw.info["sfreq"])

One would need to change the code structure if _sync_onset() is to be used. However, we can simply add assert raw.info["meas_date"] == raw.annotations.orig_time to impose the same requirement as on other formats. What do you think?

Also I can add a note to %(export_warning_note_raw)s to document the new requirement.

cbrnr · 2025-01-03T16:38:49Z

OK, if it's too complicated then don't bother trying to use _sync_onset(). But yes, good idea to add an assert and documentation (and maybe a comment explaining why we're not using _sync_onset() here)!

cbrnr · 2025-01-09T09:40:19Z

mne/utils/docs.py

+docdict["export_warning_annotations_raw"] = """\
+.. warning::
+    When exporting ``Raw`` with annotations, ``raw.info["meas_date"]`` must be the same
+    as ``raw.annotations.orig_time``. This guarantees that the annotations are in the
+    same reference frame as the samples.
+    When `Raw.first_time` is not zero (e.g., after cropping), the onsets are
+    automatically corrected so that onsets are always relative to the first sample.
+"""
+


I do not think it is necessary to add a docdict entry if this entry is only used exactly once. Please move that text directly into the docstring.

Just did. Let me know how it looks to you!

qian-chu · 2025-01-20T08:57:05Z

Hi everyone, is there anything else needed for this PR to be completed? Thanks!

cbrnr · 2025-01-20T09:33:02Z

I think this is good to go, but can you rebase please so that all tests pass?

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qian-chu · 2025-01-20T10:32:01Z

Oops, seems I've triggered a grand review request through a rookie rebase... Sorry for bugging everyone

drammock

LGTM, @cbrnr feel free to merge if you're happy!

cbrnr · 2025-01-21T18:31:15Z

Thanks @qian-chu, great contribution!

qian-chu · 2025-01-21T20:49:22Z

Thanks everyone!

* upstream/main: (57 commits) Allow lasso selection sensors in a plot_evoked_topo (mne-tools#12071) MAINT: Fix doc build (mne-tools#13076) BUG: Improve sklearn compliance (mne-tools#13065) [pre-commit.ci] pre-commit autoupdate (mne-tools#13073) MAINT: Add Numba to 3.13 test (mne-tools#13075) Bump autofix-ci/action from ff86a557419858bb967097bfc916833f5647fa8c to 551dded8c6cc8a1054039c8bc0b8b48c51dfc6ef in the actions group (mne-tools#13071) [BUG] Correct annotation onset for exportation to EDF and EEGLAB (mne-tools#12656) New feature for removing heart artifacts from EEG or ESG data using a Principal Component Analysis - Optimal Basis Sets (PCA-OBS) algorithm (mne-tools#13037) [BUG] Fix taper weighting in computation of TFR multitaper power (mne-tools#13067) [FIX] Reading an EDF with preload=False and mixed frequency (mne-tools#13069) Fix evoked topomap colorbars, closes mne-tools#13050 (mne-tools#13063) [pre-commit.ci] pre-commit autoupdate (mne-tools#13060) BUG: Fix bug with interval calculation (mne-tools#13062) [DOC] extend documentation for add_channels (mne-tools#13051) Add `combine_tfr` to API (mne-tools#13054) Add `combine_spectrum()` function and allow `grand_average()` to support `Spectrum` data (mne-tools#13058) BUG: Fix bug with helium anon (mne-tools#13056) [ENH] Add option to store and return TFR taper weights (mne-tools#12910) BUG: viz plot window's 'title' argument showed no effect. (mne-tools#12828) MAINT: Ensure limited set of tests are skipped (mne-tools#13053) ...

qian-chu requested review from cbrnr and sappelhoff as code owners June 10, 2024 20:00

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qian-chu changed the title ~~Correct annotation onset for exportation to EDF and EEGLAB~~ [BUG] Correct annotation onset for exportation to EDF and EEGLAB Jun 10, 2024

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correct annotation onset during exportation

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shristibaral and others added 12 commits January 20, 2025 11:07

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[ENH] Add option to store and return TFR taper weights (mne-tools#12910)

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Add combine_spectrum() function and allow grand_average() to supp…

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Add combine_tfr to API (mne-tools#13054)

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[DOC] extend documentation for add_channels (mne-tools#13051)

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BUG: Fix bug with interval calculation (mne-tools#13062)

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qian-chu force-pushed the correct_annot_export branch from f2381db to 96fb55d Compare January 20, 2025 10:08

qian-chu requested review from adam2392, alexrockhill, britta-wstnr, hoechenberger, jasmainak, mscheltienne, rob-luke and wmvanvliet as code owners January 20, 2025 10:08

Merge remote-tracking branch 'upstream/main' into correct_annot_export

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qian-chu deleted the correct_annot_export branch January 21, 2025 20:49

qian-chu mentioned this pull request Mar 16, 2026

[REVIEW]: SOVABIDS: EEG-to-BIDS conversion software focused on automation, reproducibility and interoperability openjournals/joss-reviews#10146

Open

	if events is not None:
	# subtract raw.first_samp because brainvision marks events starting from the
	# first available data point and ignores the raw.first_samp
	assert isinstance(events, np.ndarray), msg
	assert events.ndim == 2, msg
	assert events.shape[-1] == 3, msg
	events[:, 0] -= raw.first_samp
	events = events[:, [0, 2]] # reorder for pybv required order

	annotations = []
	for desc, onset, duration, ch_names in zip(
	raw.annotations.description,
	raw.annotations.onset,
	raw.annotations.duration,
	raw.annotations.ch_names,
	):
	if ch_names:
	for ch_name in ch_names:
	annotations.append(
	EdfAnnotation(onset, duration, desc + f"@@{ch_name}")
	)
	else:
	annotations.append(EdfAnnotation(onset, duration, desc))

	annotations = [
	raw.annotations.description,
	raw.annotations.onset,
	raw.annotations.duration,
	]

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Outputs using the current main of MNE

Original raw array

BrainVision (functions properly)

EDF

EEGLAB

Outputs using the PR branch

Original raw array

BrainVision

EDF

EEGLAB

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qian-chu commented Jun 10, 2024 •

edited

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hoechenberger commented Jul 3, 2024 •

edited

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Outputs using the current `main` of MNE