# Mne Source

> >

- **Type:** Skill
- **Install:** `agentstack add skill-exekiel179-mne-mcp-mne-source`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Exekiel179](https://agentstack.voostack.com/s/exekiel179)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Exekiel179](https://github.com/Exekiel179)
- **Source:** https://github.com/Exekiel179/MNE-MCP/tree/main/skills/mne-source

## Install

```sh
agentstack add skill-exekiel179-mne-mcp-mne-source
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# MNE Source Localization (grill → analyze → critic)

Source (inverse) modeling of neurophysiology data via the MNE MCP server. This skill is **skeptical
by design**: the inverse problem is **ill-posed** — many source configurations explain the same
sensor data — so every estimate depends on choices (head model, covariance, regularization, method)
that all run *without any error* and silently shape "where" the activity is. The single biggest trap
is a **quantitative source claim resting on a template head with no individual MRI** — so the
discipline is to **grill the model before computing and critique the localization before believing.**

> Companion skills: `mne-mcp-guard` for technical execution safety; `mne-methodology-critic` for
> Phase 3. Loaded objects persist in one MNE session. Source tools need the **`[full]`** extra
> (`nibabel` for the forward model, `pyvista` for rendering).

---

## PHASE 1 — GRILL (before computing anything)

Do **not** build a forward model or apply an inverse until these are answered. If the user can't
answer one, propose a sensible default **and explicitly flag the open risk** — never silently choose.

**The question that decides validity**
- **Template head (fsaverage) or individual MRI?** ⚠️ The MCP forward model uses **fsaverage** — a
  *template* head. That makes any source estimate **exploratory / qualitative**: it cannot support a
  quantitative anatomical claim ("the generator is in left BA44"). Quantitative localization needs an
  **individual MRI + BEM + co-registration**. (This is the single most common fatal overreach here.)

**Geometry & co-registration**
- Are electrode positions **digitized and co-registered** to the head, or **nominal** (idealized
  montage on a template)? Nominal positions add localization error on top of the template-head error.
- EEG or MEG? **EEG source localization is harder** (volume conduction, skull-conductivity
  uncertainty) and is more easily overinterpreted than MEG.

**Noise covariance (drives the whitening — get it wrong and the map is wrong)**
- Source: **pre-stimulus baseline** (for evoked) or **empty-room** (MEG)? Enough samples to estimate
  it stably (rank!)? Was the data **rank-reduced** by average reference / interpolation / ICA — and
  does the covariance reflect that rank?

**Inverse method & regularization**
- Which inverse: **MNE / dSPM / sLORETA / eLORETA** (distributed) or **LCMV / DICS** (beamformer) or
  **mixed-norm** (sparse)? Each carries a different bias — and ⚠️ **MNE/dSPM have a depth bias**
  (superficial sources favored); sLORETA/eLORETA reduce location bias; beamformers assume
  uncorrelated sources.
- **SNR / regularization (λ²)**: arbitrary or justified? λ² ≈ 1/SNR² controls spread vs noise; an
  unjustified value is a hidden free parameter.

**The spatial claim & inference**
- What is the **spatial claim**, and at what **resolution**? (a lobe? a gyrus? a single vertex?)
  Template-head EEG cannot license gyral-level claims.
- **Source-space multiple comparisons**: source maps are **smooth** → neighbouring vertices are
  **dependent** → no single-vertex/single-time claim without correction. Plan **cluster-based
  permutation** in source space, not per-vertex thresholding.

---

## PHASE 2 — ANALYZE

1. **Capability + look first.** `mne_check_status` (confirm the `[full]` extra: `nibabel`, `pyvista`);
   review the evoked/epochs you will invert (`mne_plot_evoked` / `mne_describe`) — a clean, baselined
   evoked with a sensible reference and montage is the prerequisite for a meaningful inverse.

2. **Noise covariance** from the pre-stimulus **baseline** (or empty-room for MEG):

   ```python
   # epochs already in the session; baseline = up to t=0
   noise_cov = mne.compute_covariance(epochs, tmax=0.0, method="auto", rank="info")
   ```
   (Structured form: `mne_compute_noise_cov(name="epochs", tmax=0.0, cov_name="noise_cov")`.
   `rank="info"` respects rank loss from average reference / interpolation / ICA.)

3. **Forward model** — **template head (fsaverage)**, downloaded once. State the caveat aloud:

   ```python
   # template-head EEG forward for the object's montage (fsaverage BEM)
   # -> estimates are EXPLORATORY; no individual MRI / co-registration
   ```
   (Structured form: `mne_make_forward(name="evoked", fwd_name="fwd")` — fetches fsaverage on first
   use.)

4. **Apply the inverse** (start with **dSPM**; report the peak vertex/time):

   ```python
   inv = mne.minimum_norm.make_inverse_operator(evoked.info, fwd, noise_cov)
   stc = mne.minimum_norm.apply_inverse(evoked, inv, lambda2=1.0/3.0**2, method="dSPM")
   ```
   (Structured form: `mne_apply_inverse(evoked_name="evoked", fwd_name="fwd", cov_name="noise_cov",
   method="dSPM", snr=3.0, stc_name="stc")`. Try **sLORETA/eLORETA** to check depth-bias sensitivity.)

5. **Beamformer (LCMV) / DICS / mixed-norm** via `mne_run_code` when appropriate:

   ```python
   from mne.beamformer import make_lcmv, apply_lcmv
   data_cov = mne.compute_covariance(epochs, tmin=0.0, tmax=0.3)     # active window
   filters  = make_lcmv(evoked.info, fwd, data_cov, reg=0.05, noise_cov=noise_cov)
   stc_lcmv = apply_lcmv(evoked, filters)                            # uncorrelated-source assumption
   ```
   (DICS: `make_dics` on a CSD from `csd_morlet`. Sparse: `mne.inverse_sparse.mixed_norm`.)

6. **Plot + report.** Render the cortical map and state the **peak location/time** with the
   template-head caveat:
   `mne_plot_source_estimate(stc_name="stc", hemi="both", time=None)` — **read the PNG**, then
   **archive** the equivalent code + figures (the `mne-analyst` archiving convention).

Best-practice reminders: baseline-correct and choose the reference *before* inverting; estimate
covariance with the correct **rank**; always state the **template-head / exploratory** caveat;
compare methods (dSPM vs sLORETA) rather than trusting one map.

---

## PHASE 3 — CRITIC (before believing the result)

Hand the model + result (head model, co-registration, covariance source, method, λ²/SNR, the spatial
claim) to **`mne-methodology-critic`** (invoke the skill, or dispatch it as a subagent with
`references/methodology-checklist.md`). For source work it will specifically check:

- **template-head localization**: a quantitative source claim with **no individual MRI /
  co-registration** is exploratory — WARN, or FAIL if stated quantitatively (gyral/Brodmann claim);
- **depth bias** of MNE/dSPM (superficial sources favored) — was it acknowledged / mitigated?
- **arbitrary regularization / SNR** (λ² a hidden free parameter) — justified or swept?
- **single-vertex / single-time inference without correction** — source maps are smooth →
  neighbours are dependent → **cluster-based permutation** in source space, not per-vertex;
- **misestimated noise covariance** (wrong baseline, too few samples, ignored rank loss);
- **EEG source localization overinterpreted** (volume conduction + skull conductivity uncertainty).

Report its **BLOCK / REVISE / PASS** verdict to the user and act on it before stating *where* the
activity is.

See `references/source-methods.md` for deeper recipes (covariance & rank, BEM/template vs individual
MRI, MNE/dSPM/sLORETA/eLORETA depth bias, LCMV/DICS, mixed-norm, and source-space inference choices).

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Exekiel179](https://github.com/Exekiel179)
- **Source:** [Exekiel179/MNE-MCP](https://github.com/Exekiel179/MNE-MCP)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-exekiel179-mne-mcp-mne-source
- Seller: https://agentstack.voostack.com/s/exekiel179
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.