# Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding

arXiv: https://arxiv.org/abs/2410.07189 · HTML: https://mlsanity.com/paper/2410.07189/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface](https://mlsanity.com/paper/1708.06578/) (2017)
- [A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification](https://mlsanity.com/paper/2312.09623/) (2023)
- [Deep neural networks on graph signals for brain imaging analysis](https://mlsanity.com/paper/1705.04828/) (2017)

## Built on by (most cited first, 1 of 1)

- [EEG-MFTNet: An Enhanced EEGNet Architecture with Multi-Scale Temporal Convolutions and Transformer Fusion for Cross-Session Motor Imagery Decoding](https://mlsanity.com/paper/2604.05843/) (2026)

## Abstract (arXiv)

This paper presents the novel Dual Stream Graph-Transformer Fusion (DS-GTF) architecture designed specifically for classifying task-based Magnetoencephalography (MEG) data. In the spatial stream, inputs are initially represented as graphs, which are then passed through graph attention networks (GAT) to extract spatial patterns. Two methods, TopK and Thresholded Adjacency are introduced for initializing the adjacency matrix used in the GAT. In the temporal stream, the Transformer Encoder receives concatenated windowed input MEG data and learns new temporal representations. The learned temporal and spatial representations from both streams are fused before reaching the output layer. Experimental results demonstrate an enhancement in classification performance and a reduction in standard deviation across multiple test subjects compared to other examined models.