mini-mne

A simplified wrapper around MNE-Python for teaching EEG data analysis. mini-mne exposes only the essential classes and methods, reducing the cognitive load for learners who are new to EEG preprocessing and analysis.

Installation

pip install --upgrade mini-mne

Requires Python ≥ 3.11.

Usage

mini-mne follows the standard EEG analysis pipeline: load continuous data → epoch around events → compute ERPs.

from mini_mne import SimpleRaw, SimpleEpoch, SimpleEvoked, plot_evokeds

# Load data (downloads the MNE ERP-CORE example dataset if no path is given)
raw = SimpleRaw()

# Inspect the data
print(raw)
print(raw.sampling_frequency)  # Hz
print(raw.channel_names)
print(raw.duration)            # seconds

# Preprocess
raw = raw.filter(l_freq=0.1, h_freq=40.0)
raw = raw.re_reference()

# Extract events from annotations
events, event_id = raw.get_events()

# Epoch the data
epochs = SimpleEpoch(raw, events, event_id,
                     tmin=-0.2, tmax=0.8,
                     baseline=(-0.2, 0),
                     reject={"eeg": 100e-6})

print(epochs)
print(epochs.event_types)
print(epochs.dropped_epochs)

# Compute ERPs
evokeds = epochs.average(by_event_type=True)

# Plot
plot_evokeds(evokeds)

Classes

Licence

mini-mne is licenced under the BSD-3-Clause licence.

Acknowledgements

mini-mne is built on top of MNE-Python. The default example dataset is from the ERP-CORE dataset (Kappenman et al., 2021).