| name | mne-eeg-tool |
| description | Use this skill whenever any NeuroClaw modality skill (especially eeg-skill) needs to execute concrete MNE-Python operations for EEG loading, preprocessing, filtering, artifact removal, epoching, frequency-band analysis, or feature extraction. This is the dedicated base/tool skill that contains all specific MNE-Python code and usage patterns. |
| license | MIT License (NeuroClaw custom skill – freely modifiable within the project) |
| layer | base |
| skill_type | tool |
| dependencies | ["claw-shell"] |
MNE-EEG Tool (Base/Tool Layer)
Overview
mne-eeg-tool is the NeuroClaw base/tool skill that provides all concrete MNE-Python implementation for EEG processing.
It is never called directly by the user. It is exclusively delegated to by the modality-layer skill eeg-skill (and any future EEG-related modality skills).
This skill:
- Contains the complete, ready-to-run MNE-Python code (covers all standard preprocessing and feature extraction tasks).
- Handles environment setup verification.
- Provides a single, well-documented wrapper script (
eeg_pipeline.py) that implements all common EEG tasks, including the newly added continuous-data branch, functional connectivity, ERP features, frontal alpha asymmetry, and microstate analysis.
- Routes every execution through
claw-shell for safety and logging.
Research use only — outputs are for scientific analysis.
Agent Reference Rule
When the agent needs MNE-EEG implementation code, it should first consult the curated snippet in skills/mne-eeg-tool/scripts/ instead of copying from the embedded wrapper below.
Reference snippet available:
scripts/eeg_pipeline_reference.py -> full EEG pipeline: load, bad-channel detection, filtering, ICA, epoching, frequency bands, connectivity, ERP features, alpha asymmetry, microstates
Example:
python skills/mne-eeg-tool/scripts/eeg_pipeline_reference.py \
--input path/to/data.set \
--resting \
--output-dir eeg_output/
Quick Reference (Core Functions)
| Function | Purpose | New in this update? |
|---|
load_eeg() | Load .set / .edf / .bdf / .fif / BIDS + validation | — |
detect_and_interpolate_bad_channels() | Auto-detect + interpolate noisy channels | Yes |
preprocess_filtering() | Resample + high-pass + notch + bandpass | — |
remove_artifacts() | ICA + AutoReject + EOG/ECG regression | Yes |
continuous_data_cleaning() | Resting-state pipeline (no events) | Yes |
rereference_and_epoch() | Average reference + epoching + baseline correction | — |
extract_frequency_bands() | Split into δ/θ/α/β/γ bands + power matrices | — |
extract_features() | Band power, CSP, Hjorth, sample entropy, etc. | — |
compute_connectivity() | PLV, coherence, wPLI, imaginary coherence | Yes |
extract_erp_features() | Peak amplitude, latency, area under curve | Yes |
compute_alpha_asymmetry() | Frontal alpha asymmetry (emotion studies) | Yes |
run_microstate_analysis() | EEG microstates (resting-state) | Yes |
full_eeg_pipeline() | One-click end-to-end pipeline (any combination) | — |
Installation (Handled by dependency-planner)
This skill is automatically installed when eeg-skill is used:
conda create -n neuroclaw-eeg python=3.11 -y
conda activate neuroclaw-eeg
conda install -c conda-forge mne pyentrp scikit-learn pandas numpy matplotlib -y
pip install mne[full]
NeuroClaw recommended wrapper script
The full EEG pipeline implementation is in scripts/eeg_pipeline_reference.py (see Agent Reference Rule above).
Example:
python skills/mne-eeg-tool/scripts/eeg_pipeline_reference.py \
--input path/to/data.set \
--resting \
--output-dir eeg_output/
Functions: load_eeg, detect_and_interpolate_bad_channels, preprocess_filtering, remove_artifacts, continuous_data_cleaning, rereference_and_epoch, extract_frequency_bands, extract_features, compute_connectivity, extract_erp_features, compute_alpha_asymmetry, run_microstate_analysis, full_eeg_pipeline.
Important Notes & Limitations
- Requires the
neuroclaw-eeg conda environment (auto-created by dependency-planner).
- Long-running steps (ICA, connectivity, microstates) run safely in
claw tmux session.
- Outputs are always written to
./eeg_output/ with clear subfolders.
- Fully extensible: new functions can be added to
eeg_pipeline.py without touching eeg-skill.
Complementary / Related Skills
claw-shell → executes this skill’s wrapperdependency-planner + conda-env-manager → creates neuroclaw-eeg environment
Reference
Official MNE-Python documentation (https://mne.tools) + MNE-Connectivity + mne-microstates.
Aligned with NeuroClaw base/tool skill pattern (freesurfer-tool, dcm2nii, etc.).
Curated reference snippet in this skill:
skills/mne-eeg-tool/scripts/eeg_pipeline_reference.py
Post-Execution Verification (Harness Integration)
After MNE-EEG processing completes, this skill automatically invokes harness-core's VerificationRunner to validate output integrity:
Integrated verification checks:
from skills.harness_core import VerificationRunner, AuditLogger
verifier = VerificationRunner(task_type="eeg_processing")
verifier.add_check("eeg_loading",
checker=lambda: verify_eeg_loaded(output_dir),
severity="error"
)
verifier.add_check("channel_integrity",
checker=lambda: verify_channel_count(output_dir),
severity="error"
)
verifier.add_check("artifact_removal",
checker=lambda: verify_artifact_removal_rate(output_dir, min_rate=0.85),
severity="warning"
)
verifier.add_check("frequency_spectrum",
checker=lambda: verify_frequency_spectrum(output_dir),
severity="warning"
)
verifier.add_check("data_integrity",
checker=lambda: verify_no_nan_inf(output_dir),
severity="error"
)
verifier.add_check("feature_extraction",
checker=lambda: verify_feature_dimensions(output_dir),
severity="warning"
)
report = verifier.run(output_dir)
logger = AuditLogger(log_file=f"{output_dir}/eeg_verification.jsonl")
logger.log_validation(
task_name="eeg_processing",
checks_passed=len([r for r in report.results if r.passed]),
total_checks=len(report.results),
output_path=output_dir
)
Output: eeg_output/eeg_verification.jsonl (structured audit log with JSONL format)
Created At: 2026-03-25 14:00 HKT
Last Updated At: 2026-04-05 02:03 HKT
Author: chengwang96
