Architectures / Biosignal

🧠 EEG Conformer

Conv stem + Transformer encoder — SOTA for high-channel motor imagery EEG (Song 2023)

Layers

Parameters

78.6K

Input

1 × 22 × 1000

Output

Verifier

1 advisory

Every number on this page is computed from the graph by the same functions the app runs, not written by hand.

Open EEG Conformer on the canvas Free, no account needed

When to pick it

Pick when you have ≥16 channels and ~400+ trials per subject. Best published accuracy on BCI IV-2a/2b; expect tricky regularization.

Structure

23 layers. Output shapes are propagated from the input shape, batch dimension excluded.

	Layer	Type	Parameters	Output shape
1	eeg_window	Input	shape=[1, 22, 1000]	1 × 22 × 1000
2	temporal_conv	Conv2D	outChannels=40, kernelSize=[1, 25], stride=1	40 × 22 × 976
3	spatial_conv	Conv2D	outChannels=40, kernelSize=[22, 1], stride=1	40 × 1 × 976
4	norm	BatchNorm	normalizedShape=40	40 × 1 × 976
5	act	ELU		40 × 1 × 976
6	pool	AvgPool2D	kernelSize=[1, 75], stride=[1, 15]	40 × 1 × 61
7	patch_proj	Conv2D	outChannels=40, kernelSize=[1, 1], stride=1	40 × 1 × 61
8	to_tokens	Permute		61 × 1 × 40
9	norm	LayerNorm	normalizedShape=40	61 × 1 × 40
10	self_attn	Multi-Head Attention	embedDim=40, numHeads=10	61 × 1 × 40
11	residual	Add		61 × 1 × 40
12	norm	LayerNorm	normalizedShape=40	61 × 1 × 40
13	dense	Feed Forward	embedDim=40, ffDim=160	61 × 1 × 40
14	residual	Add		61 × 1 × 40
15	norm	LayerNorm	normalizedShape=40	61 × 1 × 40
16	self_attn	Multi-Head Attention	embedDim=40, numHeads=10	61 × 1 × 40
17	residual	Add		61 × 1 × 40
18	norm	LayerNorm	normalizedShape=40	61 × 1 × 40
19	dense	Feed Forward	embedDim=40, ffDim=160	61 × 1 × 40
20	residual	Add		61 × 1 × 40
21	flatten	Flatten		2440
22	classifier	Linear	outFeatures=4	4
23	logits	Output		4

What the verifier says

The same 41 structural checks that run on every edit in the app, on this graph.

warn2 attention layer(s) present but no positional encoding found. Attention is permutation-invariant, without position information the model cannot distinguish token order. Fix: Add a PositionalEncoding (sinusoidal) or RoPE layer before the first attention layer. (self_attn)
attention-no-pe

The PyTorch it exports

Generated from the graph above. First 46 lines; the app exports the whole file, plus the training loop, the data contract and a deploy bundle.

# Architecture designed with Neurarch: https://neurarch.com
# PyTorch: compatible with Python 3.8+ and torch>=1.12
# Colab: pip install torch torchvision  (usually pre-installed)
#
# WARNING: 1 layer(s) below are not yet supported by the PyTorch
# exporter and pass their input through UNCHANGED in forward():
#   - to_tokens (permute)

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple

class EEGConformer(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv2d_1 = nn.Conv2d(1, 40, kernel_size=(1, 25), stride=1, padding=0)
        self.conv2d_2 = nn.Conv2d(40, 40, kernel_size=(22, 1), stride=1, padding=0)
        self.batchNorm_1 = nn.BatchNorm2d(40)
        self.elu_1 = nn.ELU(alpha=1)
        self.avgpool2d_1 = nn.AvgPool2d(kernel_size=(1, 75), stride=(1, 15), padding=0)
        self.conv2d_3 = nn.Conv2d(40, 40, kernel_size=(1, 1), stride=1, padding=0)
        self.layerNorm_1 = nn.LayerNorm(40)
        self.multiHeadAttention_1 = nn.MultiheadAttention(embed_dim=40, num_heads=10, batch_first=True)
        self.layerNorm_2 = nn.LayerNorm(40)
        self.feedForward_1 = nn.Sequential(
            nn.Linear(40, 160),
            nn.ReLU(),
            nn.Linear(160, 40)
        )
        self.layerNorm_3 = nn.LayerNorm(40)
        self.multiHeadAttention_2 = nn.MultiheadAttention(embed_dim=40, num_heads=10, batch_first=True)
        self.layerNorm_4 = nn.LayerNorm(40)
        self.feedForward_2 = nn.Sequential(
            nn.Linear(40, 160),
            nn.ReLU(),
            nn.Linear(160, 40)
        )
        self.linear_1 = nn.Linear(2440, 4)

    def forward(self, x):
        # eeg_window shape: [1,22,1000]
        conv2d_mporal = self.conv2d_1(x)
        conv2d_patial = self.conv2d_2(conv2d_mporal)
        batch_norm_bn_1 = self.batchNorm_1(conv2d_patial)

For agents

This architecture is machine-readable end to end. An agent can list the set, fetch this graph, edit it, and have the edit verified before any GPU time is spent.

Markdown twin Index JSON This graph as JSON MCP server POST /api/v1/check

Also in Biosignal

🧠 EEGNet

Compact CNN for EEG/BCI — depthwise + separable convs make it 10× lighter than standard CNNs

16 layers · 2.7K