---
license: mit
pipeline_tag: feature-extraction
tags:
  - eeg
  - bci
  - motor-imagery
  - eegnet
  - graph-neural-network
  - positional-encoding
  - feature-extraction
  - pytorch
---

# EEGNet-GNN-Pos feature extractor (F1=6, F2=12)

**EEGNet** with Layer 2 replaced by a graph convolution over the electrode montage, where the
montage geometry is a first-class input rather than something used once to build a fixed
adjacency and then discarded.

```
Temporal conv -> POS-GRAPH conv -> Separable conv -> Avg-pool + flatten -> flat vector
  (Layer 1)        (Layer 2)         (Layer 3)          (Layer 4)           OUTPUT
  IDENTICAL        THE SWAP          IDENTICAL          IDENTICAL
```

The model stops at Layer 4 and returns a flat feature vector; `EEGNetGNNPosClassifier` attaches
a linear head. Output size is exposed as `model.flat_dim` (**372** with the defaults).

## What is position-conditioned

`H = FiLM_pos(Â X W) + X W_res`, then BatchNorm + ELU, then a signed readout collapsing the
electrodes to one. Coordinates enter twice:

- **FiLM** — per-node scale and shift predicted from `(x, y)`. `Â` alone cannot distinguish C3
  from C4: their neighbourhoods are isomorphic, so message passing treats both hemispheres
  identically.
- **Readout** — `node_weight[:, n] = MLP(gamma(x_n, y_n))`, a continuous spatial field sampled
  at each electrode, rather than a free `(F_out, N)` table indexed by channel order.

That buys three things a lookup table cannot have: invariance to channel ordering, transfer to a
montage with a different electrode count (`transfer_to_montage`), and a spatial field that can be
evaluated between electrodes (`pattern_at`).

> The readout must stay **signed**. A softmax over electrodes is a non-negative convex
> combination and cannot build the spatial high-pass filter that isolates focal C3/C4 ERD.

## Options

| arg | default | effect |
|-----|---------|--------|
| `readout` | `"coord"` | `"coord"` / `"lookup"` (free table) / `"attention"` / `"mean"` |
| `pos_mode` | `"film"` | `"film"` / `"bias"` (shift only) / `"none"` |
| `residual` | `True` | adds un-smoothed node features back after message passing |
| `learn_adjacency` | `False` | makes `Â` a trainable parameter |
| `spatial` | `"graph"` | `"depthwise"` restores stock EEGNet for a controlled A/B |

## Usage

```python
from eegnet_gnn_pos import EEGNetGNNPos, EEGNetGNNPosClassifier
import torch

backbone = EEGNetGNNPos(spatial="graph", readout="coord")
model = EEGNetGNNPosClassifier(backbone, n_classes=4)

x = torch.randn(1, 1, 22, 1000)     # (batch, 1, channels, time)
logits = model(x)                   # (1, 4)
```

Train end-to-end. Do **not** freeze the backbone unless loading genuinely pretrained weights.

## Caveat

`readout="coord"` has more parameters than the free table it replaces (~2.1k backbone vs ~1.2k).
On ~288 trials per subject that is a real cost, and the geometric structure has to earn it. The
honest comparison is a three-way A/B against `readout="lookup"` and `spatial="depthwise"` with an
identical head, which is what Notebook 2 runs.