This is a repository for realtime analysis of MEG/EEG data with MNE. The documentation can be found here:
- numpy
- MNE
We recommend the Anaconda Python distribution. We require that you use Python 3. You may choose to install mne-realtime via pip.
Besides numpy
and scipy
(which are included in the standard Anaconda installation), you will need to install the most recent version of MNE
using the pip
tool:
$ pip install -U mne
Then install mne-realtime
:
$ pip install https://api.github.com/repos/mne-tools/mne-realtime/zipball/master
These pip
commands also work if you want to upgrade if a newer version of mne-realtime
is available. If you do not have administrator privileges on the computer, use the --user
flag with pip
.
info = mne.io.read_info(op.join(data_path, 'MEG', 'sample',
'sample_audvis_raw.fif'))
with FieldTripClient(host='localhost', port=1972,
tmax=30, wait_max=5, info=info) as rt_client:
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, ...)
rt_epochs.start()
for ev in rt_epochs.iter_evoked():
epoch_data = ev.data
# or alternatively, get last n_samples
rt_epoch = rt_client.get_data_as_epoch(n_samples=500)
continuous_data = rt_epoch.get_data()
The FieldTripClient
supports multiple vendors through the FieldTrip buffer. It can be replaced with other clients such as LSLClient
. See API section for a list of clients.
Use the github issue tracker to report bugs.