# GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition

arXiv: https://arxiv.org/abs/2605.25061 · HTML: https://mlsanity.com/paper/2605.25061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 24 May 2026.

## Builds on (most cited first, 9 of 9)

- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Spatial-Temporal Recurrent Neural Network for Emotion Recognition](https://mlsanity.com/paper/1705.04515/) (2017)
- [LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface](https://mlsanity.com/paper/2105.02786/) (2021)
- [EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2406.18345/) (2024)
- [GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals](https://mlsanity.com/paper/2006.08924/) (2020)
- [Normalized multivariate time series causality analysis and causal graph reconstruction](https://mlsanity.com/paper/2104.11360/) (2021)
- [MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning](https://mlsanity.com/paper/2407.05550/) (2024)

## Abstract (arXiv)

EEG-based emotion recognition holds significant promise for objective diagnosis of mood disorders. Graph neural networks (GNNs) have emerged as the dominant paradigm for modeling inter-channel dependencies in EEG, yet existing approaches rely on symmetric adjacency matrices derived from spatial proximity or functional correlations that fundamentally capture statistical associations rather than directed causal influences, which conflicts with the inherently asymmetric, causally-driven nature of neural information flow. To bridge this gap, we propose GL-LFGNN, a Global-Local Dual-branch Causal Graph Neural Network grounded in Liang-Kleeman information flow theory. Unlike Granger causality that merely assesses temporal precedence, our approach rigorously quantifies causal strength from a dynamical systems perspective, yielding neurophysiologically interpretable directed graphs. A dual-branch architecture further integrates whole-brain connectivity with region-specific processing aligned to established functional neuroanatomy. On the MEEG dataset, GL-LFGNN achieves 86.17% (Arousal) and 86.71% (Valence) accuracy with only 37K parameters -- approximately 10% of the current state-of-the-art -- demonstrating that principled causal modeling can simultaneously enhance interpretability, generalization, and computational efficiency. Code will be released.