This example demonstrates how to connect the MNE real-time system to the Fieldtrip buffer using FieldTripClient class.
This example was tested in simulation mode
neuromag2ft –file MNE-sample-data/MEG/sample/sample_audvis_raw.fif
using a modified version of neuromag2ft available at
http://neuro.hut.fi/~mainak/neuromag2ft-2.0.0.zip
to run the FieldTrip buffer. Then running this example acquires the data on the client side.
Since the Fieldtrip buffer does not contain all the measurement information required by the MNE real-time processing pipeline, an info dictionary must be provided to instantiate FieldTripClient. Alternatively, the MNE-Python script will try to guess the missing measurement info from the Fieldtrip Header object.
Together with RtEpochs, this can be used to compute evoked responses using moving averages.
# Author: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.viz import plot_events
from mne.realtime import FieldTripClient, RtEpochs
print(__doc__)
# select the left-auditory condition
event_id, tmin, tmax = 1, -0.2, 0.5
# user must provide list of bad channels because
# FieldTrip header object does not provide that
bads = ['MEG 2443', 'EEG 053']
plt.ion() # make plot interactive
_, ax = plt.subplots(2, 1, figsize=(8, 8)) # create subplots
with FieldTripClient(host='localhost', port=1972,
tmax=150, wait_max=10) as rt_client:
# get measurement info guessed by MNE-Python
raw_info = rt_client.get_measurement_info()
# select gradiometers
picks = mne.pick_types(raw_info, meg='grad', eeg=False, eog=True,
stim=True, exclude=bads)
# create the real-time epochs object
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax,
stim_channel='STI 014', picks=picks,
reject=dict(grad=4000e-13, eog=150e-6),
decim=1, isi_max=10.0, proj=None)
# start the acquisition
rt_epochs.start()
for ii, ev in enumerate(rt_epochs.iter_evoked()):
print("Just got epoch %d" % (ii + 1))
ev.pick_types(meg=True, eog=False)
if ii == 0:
evoked = ev
else:
evoked = mne.combine_evoked([evoked, ev], weights='nave')
ax[0].cla()
ax[1].cla() # clear axis
plot_events(rt_epochs.events[-5:], sfreq=ev.info['sfreq'],
first_samp=-rt_client.tmin_samp, axes=ax[0])
evoked.plot(axes=ax[1]) # plot on second subplot
ax[1].set_title('Evoked response for gradiometer channels'
'(event_id = %d)' % event_id)
plt.pause(0.05)
plt.draw()
plt.close()
Total running time of the script: ( 0 minutes 0.000 seconds)