Convert TRC file to mne format

Here we convert a trc file recorded with a Micromed system to fif, the common format used in mne framework.

# Author: Etienne de Montalivet <etienne.demontalivet@protonmail.com>
#
# License: BSD-3-Clause

from micromed_io.to_mne import create_mne_from_micromed_recording
from pathlib import Path

fname = Path("../data/sample.TRC")
mne_raw = create_mne_from_micromed_recording(fname)

WARNING:root:MKR channel(s) is not close to 50mV. If no trigger received, then data are corrupted.
Creating RawArray with float64 data, n_channels=14, n_times=349632
    Range : 0 ... 349631 =      0.000 ...   170.718 secs
Ready.

Info from trc file is parsed and stored in mne.Info

mne_raw.info

General

Measurement date	November 06, 2023 14:35:06 GMT
Experimenter	Unknown
Participant	Chb

Channels

Digitized points	Not available
Good channels	14 EEG
Bad channels	None
EOG channels	Not available
ECG channels	Not available

Data

Sampling frequency	2048.00 Hz
Highpass	0.00 Hz
Lowpass	1024.00 Hz

As you see, markers sent by serial connection to Micromed are parsed from the trc file and stored as mne.Annotations scalings: 7e-3 comes from Micromed +/- 3.2mV

mne_raw.plot(scalings=7e-3, duration=20, start=20)

Using matplotlib as 2D backend.

<MNEBrowseFigure size 800x800 with 4 Axes>