Incorrect number of samples error when loading a Synamps Neuroscan Curry file

Describe the bug

When trying to read the data of a Neuroscan Synamps .dat file containing a recording with 128 EEG-electrodes, I am presented with the following error:

RuntimeError: Incorrect number of samples (7623792 != 24999876), please report this error to MNE-Python developers

The file loads correctly in Matlab using EEGLAB and the loadcurry plugin. I believe this issue to be different than #8391, since the number of channels looks correct and the reported number of samples is a lot smaller than the actual number of samples.

Steps to reproduce

import mne
mne.io.read_raw_curry('subject_01.dat', preload=True)

I cannot legally share the .dat data file, but the accompanying metadata files in the same directory can be found here:

• .ceo: https://cloud.vandenkerchove.com/index.php/s/4L8dKCNtCHwwSLw
• .dap: https://cloud.vandenkerchove.com/index.php/s/MKKxGZYkK4MYQET
• .rs3: https://cloud.vandenkerchove.com/index.php/s/oYBzFA6RMDBAg4b

Expected results

The file loads correctly

Actual results

Leaving device<->head transform as None (no landmarks found)
Reading 0 ... 1748799  =      0.000 ...  1748.799 secs...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<decorator-gen-242>", line 24, in read_raw_curry
  File "/data/Workspace/PhD/mne-python/mne/io/curry/curry.py", line 470, in read_raw_curry
    return RawCurry(fname, preload, verbose)
  File "<decorator-gen-243>", line 24, in __init__
  File "/data/Workspace/PhD/mne-python/mne/io/curry/curry.py", line 503, in __init__
    super(RawCurry, self).__init__(
  File "<decorator-gen-179>", line 24, in __init__
  File "/data/Workspace/PhD/mne-python/mne/io/base.py", line 254, in __init__
    self._preload_data(preload)
  File "/data/Workspace/PhD/mne-python/mne/io/base.py", line 534, in _preload_data
    self._data = self._read_segment(
  File "<decorator-gen-181>", line 24, in _read_segment
  File "/data/Workspace/PhD/mne-python/mne/io/base.py", line 419, in _read_segment
    _ReadSegmentFileProtector(self)._read_segment_file(
  File "/data/Workspace/PhD/mne-python/mne/io/base.py", line 2013, in _read_segment_file
    return self.__raw.__class__._read_segment_file(
  File "/data/Workspace/PhD/mne-python/mne/io/curry/curry.py", line 533, in _read_segment_file
    _read_segments_file(
  File "/data/Workspace/PhD/mne-python/mne/io/utils.py", line 222, in _read_segments_file
    raise RuntimeError('Incorrect number of samples (%s != %s), '
RuntimeError: Incorrect number of samples (7623792 != 24999876), please report this error to MNE-Python developers

Additional information

Platform:      Linux-5.11.11-200.fc33.x86_64-x86_64-with-glibc2.32
Python:        3.9.2 (default, Feb 20 2021, 00:00:00)  [GCC 10.2.1 20201125 (Red Hat 10.2.1-9)]
Executable:    /data/Workspace/PhD/mne-python/.venv/bin/python
CPU:           x86_64: 12 cores
Memory:        Unavailable (requires "psutil" package)
mne:           0.23.dev0
numpy:         1.20.2 {blas=openblas, lapack=openblas}
scipy:         1.6.2
matplotlib:    Not found

sklearn:       Not found
numba:         Not found
nibabel:       Not found
nilearn:       Not found
dipy:          Not found
cupy:          Not found
pandas:        Not found
mayavi:        Not found
pyvista:       Not found
vtk:           Not found
``

[arnevdk]

The .dat file contains 830491200 bytes with encoding binary. Here is a version of the file with all bytes overwritten with 0: https://cloud.vandenkerchove.com/index.php/s/XaPobpicDkGQrJf . After inispection, it looks like ignoring the error does not result in missing samples or channels, or corrupted samples or channels, but I might be missing some minor corruption. The number of samples loaded corresponds to the number of samples in the .dap file.

[arnevdk]

The file links have expired. These should stay active:

.ceo: https://cloud.vandenkerchove.com/index.php/s/sjo5axorkRXjAzq .dap: https://cloud.vandenkerchove.com/index.php/s/ncKgzCc9NWcw4Bq .rs3: https://cloud.vandenkerchove.com/index.php/s/kTjW4Z7daZ3y95n