EEGNet-GNN feature extractor (F1=6, F2=12)
Standard EEGNet with one change: Layer 2's depthwise spatial conv is replaced by a graph convolution over the electrode montage, so electrodes mix according to how close they are on the scalp rather than as a flat, order-agnostic channel list.
Temporal conv -> 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; EEGNetGNNClassifier
attaches a linear head. Output size is exposed as model.flat_dim
(372 with the defaults: F1=6, F2=12, pool1=4, pool2=8, n_times=1000).
The swap
Standard EEGNet's Layer 2 learns, per output map, a single signed weighted sum over
all electrodes — topology is ignored, but the signs let it compute contrasts. Here that
becomes H =  X W + X W_res, where  is the symmetric-normalised adjacency of the
montage. Each electrode aggregates from its physical neighbours (e.g. C3 from
FC3, FC1, C5, C1, CP3, CP1); a residual path preserves un-smoothed focal detail; then a
learned signed, per-feature-map readout (F2, N) collapses electrodes to the same
(F2, 1, T) shape the depthwise conv produced, so Layers 1/3/4 are unchanged.
The readout must stay signed. An earlier version used
softmax(node_weight), giving a non-negative convex combination shared across all feature maps. That cannot represent a difference between electrodes, and the C3-vs-C4 ERD asymmetry is the dominant motor imagery feature — so the model sat at chance (~25% on 4-class) no matter how long it trained.
spatial="depthwise" restores the original EEGNet for a controlled A/B.
Options
| arg | default | effect |
|---|---|---|
readout | "weighted" | "weighted" (signed per-map) / "attention" / "mean" |
residual | True | adds un-smoothed node features back after message passing |
learn_adjacency | False | makes  a trainable parameter |
pool1, pool2 | 4, 8 | two-stage temporal pooling, as in EEGNet-8,2 |
Usage
from eegnet_gnn import EEGNetGNN, EEGNetGNNClassifier
import torch
backbone = EEGNetGNN(spatial="graph", F1=6, F2=12)
model = EEGNetGNNClassifier(backbone, n_classes=4)
x = torch.randn(1, 1, 22, 1000) # (batch, 1, channels, time)
logits = model(x) # (1, 4)
Train the whole thing end-to-end. Do not freeze the backbone unless you are loading genuinely pretrained weights — freezing a random init reduces the model to logistic regression on random projections.