Note
Click here to download the full example code
Reading XDF EEG data¶
Here we read some sample XDF data. Although we do not analyze it here, this recording is of a short parallel auditory response (pABR) experiment 1 and was provided by the Maddox Lab.
# Authors: Clemens Brunner <clemens.brunner@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import pyxdf
import mne
from mne.datasets import misc
fname = op.join(
misc.data_path(), 'xdf',
'sub-P001_ses-S004_task-Default_run-001_eeg_a2.xdf')
streams, header = pyxdf.load_xdf(fname)
data = streams[0]["time_series"].T
assert data.shape[0] == 5 # four raw EEG plus one stim channel
data[:4:2] -= data[1:4:2] # subtract (rereference) to get two bipolar EEG
data = data[::2] # subselect
data[:2] *= (1e-6 / 50 / 2) # uV -> V and preamp gain
sfreq = float(streams[0]["info"]["nominal_srate"][0])
info = mne.create_info(3, sfreq, ["eeg", "eeg", "stim"])
raw = mne.io.RawArray(data, info)
raw.plot(scalings=dict(eeg=100e-6), duration=1, start=14)
Out:
Creating RawArray with float64 data, n_channels=3, n_times=2320128
Range : 0 ... 2320127 = 0.000 ... 232.013 secs
Ready.
References¶
- 1
Melissa J. Polonenko and Ross K. Maddox. The Parallel Auditory Brainstem Response. Trends in Hearing, 23:2331216519871395, 2019. doi:10.1177/2331216519871395.
Total running time of the script: ( 0 minutes 5.496 seconds)
Estimated memory usage: 229 MB