MNE EEG Preprocessing Starter
SkillAI & modelsUse this skill to create a tiny synthetic EEG recording with MNE-Python, apply a simple band-pass filter, and summarize the preprocessing effect.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the MNE EEG Preprocessing Starter skill
About this capability
A framework for discovering, compiling, and validating reusable skills for scientific agents.
What this skill tells your AI
The instructions your AI receives, as published by ma-compbio-lab/skillfoundry in skills/neuroscience-and-neuroimaging/mne-eeg-preprocessing-starter/SKILL.md and read by ahel’s review.
Use this skill to create a tiny synthetic EEG recording with MNE-Python, apply a simple band-pass filter, and summarize the preprocessing effect.
What it does
- Builds a deterministic two-channel
RawArraywith oscillatory signal plus low-frequency drift. - Applies a basic
1-30 Hzband-pass filter. - Returns compact JSON with sampling rate, channel names, and before/after dispersion summaries.
When to use it
- You need a runnable starter for
EEG / MEG preprocessing. - You want a verified local
MNE-Pythonexample before working on real electrophysiology recordings.
Example
slurm/envs/neuro/bin/python skills/neuroscience-and-neuroimaging/mne-eeg-preprocessing-starter/scripts/run_mne_eeg_preprocessing.py \
--out scratch/neuro/mne_preprocessing_summary.json
Verification
- Skill-local tests:
python3 -m unittest discover -s skills/neuroscience-and-neuroimaging/mne-eeg-preprocessing-starter/tests -p 'test_*.py' - Repository smoke:
python3 -m unittest tests.smoke.test_phase31_frontier_leaf_conversion_skills -v
Signals
- GitHub stars
- 39
- Forks
- 5
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
mne-eeg-preprocessing-starter- Source
- github.com/ma-compbio-lab/skillfoundry