mne

# MNE - Neurophysiology Analysis MNE provides sophisticated tools for filtering brain signals, epoching data, and performing source localization (mapping signals back to brain anatomy). ## When to Use - Processing EEG/MEG recordings from clinical or research studies. - Analyzing event-related potentials (ERPs). - Source localization (finding where in the brain signals originate). - Connectivity analysis between brain regions. - Preprocessing neurophysiological data for machine learning. ## Core Principles ### Raw → Epochs → Evoked The standard pipeline: continuous raw data → segmented epochs → averaged evoked responses. ### Sensor Space vs. Source Space Sensor space: signals at electrodes. Source space: signals reconstructed at brain locations. ### Frequency Analysis Brain signals are analyzed in frequency bands (delta, theta, alpha, beta, gamma). ## Quick Reference ### Standard Imports ```python import mne import numpy as np ``` ### Basic Patterns ```python # 1. Load data raw = mne.io.read_raw_fif("sample_audvis_raw.fif") # Or: raw = mne.io.read_raw_edf("eeg.edf") # 2. Filter and cleaning raw.filter(l_freq=1, h_freq=40) # Bandpass filter raw.notch_filter(freqs=[50, 100]) # Remove power line noise # 3. Find events and create Epochs events = mne.find_events(raw) epochs = mne.Epochs(raw, events, event_id={'stimulus': 1}, tmin=-0.2, tmax=0.5) epochs.average().plot() # Plot Evoked potential # 4. Frequency analysis epochs.compute_psd().plot() ``` ## Critical Rules ### ✅ DO - **Filter before epoching** - Apply filters to continuous data, not epochs. - **Check data quality** - Use `raw.plot()` to visually inspect for artifacts. - **Set montage** - Assign electrode positions for proper visualization. - **Reject bad epochs** - Remove epochs with artifacts before averaging. ### ❌ DON'T - **Don't filter too aggressively** - Over-filtering removes signal along with noise. - **Don't ignore reference** - EEG signals are relative. Know your reference electrode. - **Don't mix sampling rates** - Ensure all channels have the same sampling rate. ## Advanced Patterns ### Source Localization ```python # Compute forward solution and inverse fwd = mne.make_forward_solution(raw.info, trans, src, bem) inv = mne.minimum_norm.make_inverse_operator(raw.info, fwd, cov) stc = mne.minimum_norm.apply_inverse(evoked, inv) stc.plot() ``` ### Connectivity Analysis ```python from mne.connectivity import spectral_connectivity # Compute connectivity between channels con, freqs, times, n_epochs, n_tapers = spectral_connectivity( epochs, method='coh', mode='multitaper') ``` MNE is the gold standard for neurophysiological data analysis, enabling researchers to extract meaningful insights from the complex signals of the human brain.