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).Âalone 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  a trainable parameter |
spatial | "graph" | "depthwise" restores stock EEGNet for a controlled A/B |
Usage
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.