Getting impedances from raw files

Many EEG systems provide impedance measurements for each channel within their file format. MNE does not parse this information and does not store it in the Raw object. However, it is possible to extract this information from the raw data and store it in a separate data structure.

ANT Neuro

The .cnt file format from ANT Neuro stores impedance information in the form of triggers. The function mne.io.read_raw_ant() reads this information and marks the time-segment during which an impedance measurement was performed as Annotations with the description set in the argument impedance_annotation. However, it doesn’t extract the impedance values themselves. To do so, use the function antio.parser.read_triggers.

# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

from antio import read_cnt
from antio.parser import read_triggers
from matplotlib import pyplot as plt

from mne.datasets import testing
from mne.io import read_raw_ant
from mne.viz import plot_topomap

fname = testing.data_path() / "antio" / "CA_208" / "test_CA_208.cnt"
cnt = read_cnt(fname)
_, _, _, impedances, _ = read_triggers(cnt)

raw = read_raw_ant(fname, eog=r"EOG")
impedances = [{ch: imp[k] for k, ch in enumerate(raw.ch_names)} for imp in impedances]
print(impedances[0])  # impedances measurement at the beginning of the recording

Reading ANT file /home/circleci/mne_data/MNE-testing-data/antio/CA_208/test_CA_208.cnt
All 63 EEG channels are referenced to CPz.
{'Fp1': 2.254, 'Fpz': 2.926, 'Fp2': 2.693, 'F7': 13.045, 'F3': 0.902, 'Fz': 2.737, 'F4': 0.149, 'F8': 25.132999, 'FC5': 0.304, 'FC1': 38.924, 'FC2': 2.258, 'FC6': 0.135, 'M1': 4.445, 'T7': 6.233, 'C3': 8.322, 'Cz': 0.064, 'C4': 1.271, 'T8': 4.049, 'M2': 1.846, 'CP5': 2.721, 'CP1': 2.944, 'CP2': 1.218, 'CP6': 0.879, 'P7': 22.726999, 'P3': 9.176, 'Pz': 1.84, 'P4': 27.167, 'P8': 1.157, 'POz': 3.575, 'O1': 2.698, 'O2': 8.073, 'EOG': 15.205, 'AF7': 5.842, 'AF3': 1.303, 'AF4': 2.136, 'AF8': 0.787, 'F5': 0.863, 'F1': 1.696, 'F2': 1.616, 'F6': 0.478, 'FC3': 6.01, 'FCz': 0.72, 'FC4': 4.314, 'C5': 10.945, 'C1': 6.151, 'C2': 0.772, 'C6': 0.879, 'CP3': 12.115, 'CP4': 1.887, 'P5': 14.045, 'P1': 2.733, 'P2': 34.641998, 'P6': 1.765, 'PO5': 5.666, 'PO3': 14.12, 'PO4': 2.599, 'PO6': 11.339, 'FT7': 13.158, 'FT8': 1.046, 'TP7': 5.917, 'TP8': 2.61, 'PO7': 5.691, 'PO8': 1.759, 'Oz': 5.165, 'BIP1': 2147483.75, 'BIP2': 2147483.75, 'BIP3': 2147483.75, 'BIP4': 2147483.75, 'BIP5': 2147483.75, 'BIP6': 2147483.75, 'BIP7': 2147483.75, 'BIP8': 2147483.75, 'BIP9': 2147483.75, 'BIP10': 2147483.75, 'BIP11': 2147483.75, 'BIP12': 2147483.75, 'BIP13': 2147483.75, 'BIP14': 2147483.75, 'BIP15': 2147483.75, 'BIP16': 2147483.75, 'BIP17': 2147483.75, 'BIP18': 2147483.75, 'BIP19': 2147483.75, 'BIP20': 2147483.75, 'BIP21': 2147483.75, 'BIP22': 2147483.75, 'BIP23': 2147483.75, 'BIP24': 2147483.75}

Note that the impedance measurement contains all channels, including the bipolar ones. We can visualize the impedances on a topographic map; below we show a topography of impedances before and after the recording for the EEG channels only.

raw.pick("eeg").set_montage("standard_1020")
impedances = [{ch: imp[ch] for ch in raw.ch_names} for imp in impedances]

f, ax = plt.subplots(1, 2, layout="constrained", figsize=(10, 5))
f.suptitle("Impedances (kOhm)")
impedance = list(impedances[0].values())
plot_topomap(
    impedance,
    raw.info,
    vlim=(0, 50),
    axes=ax[0],
    show=False,
    names=[f"{elt:.1f}" for elt in impedance],
)
ax[0].set_title("Impedances at the beginning of the recording")
impedance = list(impedances[0].values())
plot_topomap(
    impedance,
    raw.info,
    vlim=(0, 50),
    axes=ax[1],
    show=False,
    names=[f"{elt:.1f}" for elt in impedance],
)
ax[1].set_title("Impedances at the end of the recording")
plt.show()

In this very short test file, the impedances are stable over time.