mne-eeg

What problem does it solve?

It solves the challenge of turning raw EEG/MEG recordings into interpretable neural metrics by providing a complete, repeatable analysis workflow (preprocessing, artifact removal, ERP, time-frequency, and resting-state PSD) instead of leaving you to stitch steps together manually.

Core Features & Use Cases

• Full M/EEG preprocessing pipeline: load common EEG formats, apply montage handling, notch + bandpass filtering, re-referencing, bad-channel interpolation, optional resampling.
• ICA-based artifact removal: fit ICA with MNE conventions, detect and exclude EOG/ECG components, and return cleaned raw data.
• Event-related and spectral analyses: epoch around annotated or stim-derived events, compute condition-specific ERPs, estimate Morlet TFR, and compute band-averaged PSD (delta/theta/alpha/beta/gamma) for resting-state comparisons.
• Use Case: Analyze an auditory oddball experiment by preprocessing raw EEG, removing eye/heart artifacts with ICA, extracting ERPs for left/right conditions, and quantifying peak N100 and P300 amplitudes at a target scalp channel.

Quick Start

Use the mne-eeg skill to compute ERP and band power from your EEG file by instructing it to load your recording, run preprocessing plus ICA cleaning, epoch events into conditions, then generate ERPs, a Morlet time-frequency map, and delta/theta/alpha/beta/gamma PSD outputs.