Compute real-time evoked responses with FieldTrip clientΒΆ

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 here 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.

Evoked response for gradiometer channels(event_id = 1)
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
Running subprocess: neuromag2ft --file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif --speed 10
FieldTripClient: Waiting for server to start
Exception ignored in: <socket.socket fd=13, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('0.0.0.0', 35660)>
Traceback (most recent call last):
  File "/home/circleci/project/mne_realtime/externals/FieldTrip.py", line 227, in connect
    self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
ResourceWarning: unclosed <socket.socket fd=13, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('0.0.0.0', 35660)>
FieldTripClient: Connected
FieldTripClient: Retrieving header
FieldTripClient: Header retrieved
Not setting metadata
Setting baseline interval to [-0.19979521315838786, 0.0] sec
Applying baseline correction (mode: mean)
0 projection items activated
Waiting for epoch 1
Just got epoch 1
Waiting for epoch 2
Just got epoch 2
Waiting for epoch 3
Just got epoch 3
Waiting for epoch 4
Just got epoch 4
Waiting for epoch 5
Just got epoch 5
Waiting for epoch 6
Just got epoch 6
Waiting for epoch 7
Just got epoch 7
Waiting for epoch 8
Just got epoch 8
Waiting for epoch 9
Just got epoch 9
Waiting for epoch 10
Time of 2.0 seconds exceeded.

# Author: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)

import os.path as op
import matplotlib.pyplot as plt
import subprocess

import mne
from mne.viz import plot_events
from mne.utils import running_subprocess

from mne_realtime import FieldTripClient, RtEpochs

print(__doc__)

# user should provide info and list of bad channels because
# FieldTrip header object does not provide them
data_path = mne.datasets.sample.data_path()
info = mne.io.read_info(op.join(data_path, 'MEG', 'sample',
                                'sample_audvis_raw.fif'))

# 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']

_, ax = plt.subplots(2, 1, figsize=(8, 8))  # create subplots

speedup = 10
command = ["neuromag2ft", "--file",
           "{}/MEG/sample/sample_audvis_raw.fif".format(data_path),
           "--speed", str(speedup)]
with running_subprocess(command, after='kill',
                        stdout=subprocess.PIPE, stderr=subprocess.PIPE):
    with FieldTripClient(host='localhost', port=1972,
                         tmax=30, wait_max=5, info=info) 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 and start acquisition
        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=2.0, proj=None)
        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])

            # plot on second subplot
            evoked.plot(axes=ax[1], selectable=False, time_unit='s')
            ax[1].set_title('Evoked response for gradiometer channels'
                            '(event_id = %d)' % event_id)

            plt.pause(0.05 / speedup)
            plt.draw()
        rt_epochs.stop()

Total running time of the script: ( 0 minutes 18.175 seconds)

Estimated memory usage: 32 MB

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