eegkit / mne-realtime

Realtime data analysis with MNE-Python

MNE-realtime

This is a package for realtime analysis of MEG/EEG data with MNE. The documentation can be found here:

• Examples
• API

Dependencies

• numpy
• scipy
• MNE

Installation

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/main

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.

Quickstart

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 for a list of clients.

Bug reports

Use the github issue tracker to report bugs.