Mne Python Guide

What it does

When your agent uses it

• The user works with EEG/MEG/sEEG/ECoG/NIRS/eye-tracking data in Python
• Needs neurophysiological analysis guidance

Example prompts

• “/mne-python-guide”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1State the research question — What is the user investigating?
2. 2Justify the method choice — Confirm MNE-Python fits their data type and goal.
3. 3Declare expected outcomes — What output format? (ERP plots, TFR maps, source maps, decoding accuracy)
4. 4Note assumptions and limitations — Data quality, sample size, MRI availability, montage info.
5. 5Present the plan and WAIT for confirmation before writing code.

What it can do on your machine

Read from SKILL.md and the folder at commit 93f6855. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • github.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mne Python Guide loads about 2.3k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 116 tokens; SKILL.md has 706 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~116

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 706 words, ~2,303 tokens.

name

mne-python-guide

description

Domain-validated pipeline guidance for EEG/MEG data analysis using MNE-Python: data loading, preprocessing (filtering, ICA, re-referencing), epoching, ERP/ERF computation, time-frequency decomposition, source localization, decoding/MVPA, statistical testing, simulation, and visualization. Use this skill whenever the user works with EEG/MEG/sEEG/ECoG/NIRS/eye-tracking data in Python, mentions MNE, or needs neurophysiological analysis guidance.

domain

neuroimaging-methods

version

2.0.0

authors

Claude (AI-assisted)

papers

Gramfort et al., 2013, Gramfort et al., 2014, Luck, 2014, Jas et al., 2018, Dale et al., 2000, Pascual-Marqui, 2002, Tallon-Baudry et al., 1997, Maris &…

dependencies.required

research-literacy

review_status

ai-generated

Raw → Mark bad channels → Filter → ICA → Re-reference → Resample
  → Epochs → Evoked (ERP/ERF)
  → Time-Frequency (TFR/PSD)
  → Source Localization (MNE/dSPM/LCMV)
  → Decoding (MVPA)
  → Statistics (cluster permutation)

import mne
import numpy as np

# 1. Load
raw = mne.io.read_raw_fif('data_raw.fif', preload=True)
# or: raw = mne.io.read_raw_edf('data.edf', preload=True)

# 2. Preprocess
raw.filter(l_freq=0.1, h_freq=40.)           # bandpass
raw.notch_filter(freqs=[50, 100])              # line noise
ica = mne.preprocessing.ICA(n_components=20, random_state=97, max_iter=800)
ica.fit(raw.copy().filter(l_freq=1., h_freq=None))  # fit on 1 Hz highpass copy
eog_idx, _ = ica.find_bads_eog(raw)
ica.exclude = eog_idx
ica.apply(raw)
raw.set_eeg_reference('average')

# 3. Epoch
events, event_id = mne.events_from_annotations(raw)
epochs = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5,
                    baseline=(None, 0), preload=True,
                    reject=dict(eeg=150e-6))

# 4. ERP
evoked = epochs['target'].average()
evoked.plot_joint()

# 5. Time-frequency
freqs = np.arange(4, 30, 2)
power = epochs.compute_tfr(method="morlet", freqs=freqs, n_cycles=freqs / 2.)
power.plot()

# 6. Source localization (requires anatomy)
noise_cov = mne.compute_covariance(epochs, tmax=0., method='auto')
fwd = mne.read_forward_solution('sample-fwd.fif')
inv = mne.minimum_norm.make_inverse_operator(epochs.info, fwd, noise_cov)
stc = mne.minimum_norm.apply_inverse(evoked, inv, lambda2=1./9., method='dSPM')

# 7. Decoding
from mne.decoding import SlidingEstimator, cross_val_multiscore
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
X = epochs.get_data(copy=True)
y = epochs.events[:, -1]
clf = make_pipeline(StandardScaler(), LogisticRegression(solver='liblinear'))
slider = SlidingEstimator(clf, scoring='roc_auc')
scores = cross_val_multiscore(slider, X, y, cv=5)

# 8. Statistics
from mne.stats import spatio_temporal_cluster_test
adjacency, _ = mne.channels.find_ch_adjacency(epochs.info, 'eeg')
T_obs, clusters, p_values, H0 = spatio_temporal_cluster_test(
    [X_cond1, X_cond2], adjacency=adjacency, n_permutations=1000)

© NeuroAIHub, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (references) in packages/skills/skills/05_EEG_ERP/mne-python-guide of NeuroAIHub/BrainPilot.

• SKILL.md
• references/decoding.md
• references/io_formats.md
• references/preprocessing.md
• references/simulation.md
• references/source_localization.md
• references/statistics.md
• references/time_frequency.md
• references/visualization.md

Open the folder on GitHubat commit 93f6855