git:20260325.72d2cbf to git:20260328.891ed93

0 added, 2 removed. Audit A to A.

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name: eeg-skill

description: "Use this skill whenever the user wants to load, preprocess, epoch, filter, or extract features from EEG data (resting-state, task-based, BCI, clinical, motor imagery, emotion, epilepsy, fatigue, etc.). Triggers include: 'eeg', 'EEG preprocessing', 'EEG feature extraction', 'band power', 'downsample to frequency bands', 'motor imagery BCI', 'emotion EEG', 'epilepsy detection', or any request involving .set/.edf/.bdf/.fif/.bids files."

license: MIT License (NeuroClaw custom skill – freely modifiable within the project)

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# EEG Skill (Modality Layer)

## Overview

`eeg-skill` is the NeuroClaw **modality-layer** interface skill responsible for all EEG data processing tasks.

It strictly follows the NeuroClaw hierarchical design principles:

- This skill **only describes WHAT needs to be done** and **which tool skill to delegate to**.

- It contains **no full implementation code**.

- All concrete execution (MNE-Python calls, torchaudio, scipy, file I/O, etc.) is delegated to the dedicated base/tool skill `mne-eeg-tool`.

- Waveform-to-spectrogram conversion uses `torchaudio.transforms.MelSpectrogram`.

- Frequency-band energy extraction uses continuous wavelet transform (`scipy.signal.cwt` with `morlet2` wavelet).

**Core workflow (never bypassed):**

1. Identify the user-provided EEG files (BIDS, .set, .edf, .bdf, .fif, etc.).

2. Generate a **numbered execution plan** that clearly states WHAT needs to be done and which tool skill will handle each step.

3. Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation (“YES” / “execute” / “proceed”).

4. On confirmation, delegate every step to `mne-eeg-tool` via `claw-shell`.

5. After execution, save all outputs in a clean directory structure (`eeg_output/`).

**Research use only** — outputs are for scientific analysis only.

## Quick Reference (Common EEG Tasks – Updated 2026-03-25)

|-------------------------------------------|----------------------------------------------------------------------------|-----------------------------------------------|------------------------------------------|

| Advanced features | Functional connectivity, ERP peaks/latency/AUC, frontal alpha asymmetry, microstates | `claw-shell` (via `mne-eeg-tool`) | Connectivity matrices, ERP CSV, asymmetry .npy, microstates .fif |

| Full end-to-end pipeline | Any combination of the above for BCI, emotion, epilepsy, fatigue, etc. | `claw-shell` + `dependency-planner` | Complete processed dataset + QC report |

## Installation (Handled by dependency-planner)

No manual installation required.

When first used, `eeg-skill` automatically calls `dependency-planner` to create the isolated `neuroclaw-eeg` conda environment containing MNE-Python, torchaudio, scipy, and all required packages.

## NeuroClaw recommended wrapper script

```python

# Example snippets (for reference in mne-eeg-tool implementation)

# 1. Waveform to Mel-Spectrogram

import torch

import torchaudio.transforms as T

mel_spec = T.MelSpectrogram(

sample_rate=256, # Adjust according to your EEG sampling rate

n_fft=1024,

hop_length=256,

n_mels=128,

f_min=0.5,

f_max=60.0 # Common EEG frequency range

)

spectrogram = mel_spec(waveform) # waveform shape: (channels, time)

# 2. Frequency-band energy extraction using CWT + morlet2

import numpy as np

from scipy.signal import cwt, morlet2

def extract_band_power(signal, fs=256):

widths = np.arange(1, 128) # Adjust according to frequency range

cwt_matrix = cwt(signal, morlet2, widths)

# Example: extract delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (30-60 Hz)

delta_power = np.mean(np.abs(cwt_matrix[low_idx:high_idx])**2, axis=0)

# ... similar processing for other bands

return band_powers

```

## Important Notes & Limitations

- This SKILL.md contains **only high-level task descriptions and delegation instructions**.

- Waveform-to-spectrogram conversion is handled by `torchaudio.transforms.MelSpectrogram`.

- Frequency-band energy extraction is performed via continuous wavelet transform (`scipy.signal.cwt` + `morlet2` wavelet).

- Long-running operations (ICA on long recordings, CWT on high-density data, connectivity matrices, microstate analysis) are automatically routed to background mode in the `claw` tmux session.

- Execution begins **only after explicit user confirmation** of the full numbered plan.

- All outputs are saved in `./eeg_output/` with clear subfolders (raw/, filtered/, epoched/, features/, spectrograms/, etc.).

## When to Call This Skill

- The user provides raw or partially processed EEG data and requests preprocessing, Mel-spectrogram conversion, frequency-band energy extraction, feature engineering, or a full pipeline.

- After `research-idea` or `method-design` when the experiment involves EEG data.

## Complementary / Related Skills

- `dependency-planner` + `conda-env-manager` → environment and package installation (MNE-Python + torchaudio + scipy)

- - `claw-shell` → all actual execution

- `mne-eeg-tool` → base/tool layer that contains all specific implementation code

- - `dcm2nii` → convert clinical EEG DICOM series to NIfTI/EDF when raw data arrives in DICOM format

## Reference & Source

Aligned with NeuroClaw modality-skill pattern (see `freesurfer-tool`, `wmh-segmentation`, etc.).

Core libraries: MNE-Python (main), `torchaudio.transforms.MelSpectrogram` (waveform to spectrogram), `scipy.signal.cwt` + `morlet2` (frequency band energy extraction).

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Created At: 2026-03-25 16:00 HKT

Last Updated At: 2026-03-25 22:07 HKT

Author: Cheng Wang