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

• URL: http://arxiv.org/abs/2605.25061v1
• Date: Sun, 24 May 2026 13:11:55 GMT
• Title: GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition
• Abstract summary: We propose GL-LFGNN, a Global-Local Dual-branch Causal Graph Neural Network grounded in Liang-Kleeman information flow theory.<n>Unlike Granger causality that merely assesses temporal precedence, our approach rigorously quantifies causal strength from a dynamical systems perspective.<n>On the MEEG dataset, GL-LFGNN achieves 86.17% (Arousal) and 86.71% (Valence) accuracy with only 37K parameters.
• Score: 3.032674692886751
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS [39.37223141220675]
  Multimodal neuroimaging enables non-invasive analysis of brain networks using graph neural networks.<n> demographic factors such as age and sex systematically confound the relationship between brain connectivity and clinical outcomes.<n>We propose Artemis, a region-level causal framework that bridges this gap with causal intervention at each brain region independently.
  arXiv  Detail & Related papers  (2026-06-10T05:10:36Z)
• Kirchhoff-Inspired Neural Networks for Evolving High-Order Perception [51.28469959548178]
  We propose the Kirchhoff-Inspired Neural Network (KINN), a state-variable-based network architecture constructed based on Kirchhoff's current law.<n>KINN derives numerically stable state updates from fundamental ordinary differential equations, enabling the explicit decoupling and encoding of higher-order evolutionary components.
  arXiv  Detail & Related papers  (2026-03-25T06:17:31Z)
• LUMINA: Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis via Quad-Stream GCN [14.397085001122525]
  We propose a Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis.<n>Our model is a Quad-Stream GCN that employs a bipolar RELU activation and a dual-spectrum graph Laplacian filtering mechanism.<n>LUMINA demonstrates stable diagnostic performance in two of the most critical neurodevelopmental disorder in childhood, ADHD and ASD.
  arXiv  Detail & Related papers  (2026-03-05T04:21:52Z)
• General Self-Prediction Enhancement for Spiking Neurons [71.01912385372577]
  Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility.<n>We propose a self-prediction enhanced spiking neuron method that generates an internal prediction current from its input-output history to modulate membrane potential.<n>This design offers dual advantages, it creates a continuous gradient path that alleviates vanishing gradients and boosts training stability and accuracy, while also aligning with biological principles, which resembles distal dendritic modulation and error-driven synaptic plasticity.
  arXiv  Detail & Related papers  (2026-01-29T15:08:48Z)
• Neuronal Group Communication for Efficient Neural representation [85.36421257648294]
  This paper addresses the question of how to build large neural systems that learn efficient, modular, and interpretable representations.<n>We propose Neuronal Group Communication (NGC), a theory-driven framework that reimagines a neural network as a dynamical system of interacting neuronal groups.<n>NGC treats weights as transient interactions between embedding-like neuronal states, with neural computation unfolding through iterative communication among groups of neurons.
  arXiv  Detail & Related papers  (2025-10-19T14:23:35Z)
• Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks [0.0]
  Inferring synaptic connectivity from neural population activity is a fundamental challenge in computational neuroscience.<n>We propose a graph-based neural inference model that simultaneously predicts neural activity and infers latent connectivity by modeling neurons as interacting nodes in a graph.
  arXiv  Detail & Related papers  (2025-09-21T17:46:43Z)
• CodeBrain: Towards Decoupled Interpretability and Multi-Scale Architecture for EEG Foundation Model [52.466542039411515]
  EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models.<n>We present CodeBrain, a two-stage EFM designed to fill this gap.<n>In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens.<n>In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention.
  arXiv  Detail & Related papers  (2025-06-10T17:20:39Z)
• On the Relationship Between Robustness and Expressivity of Graph Neural Networks [7.161966906570077]
  Graph Neural Networks (GNNs) are vulnerable to bit-flip attacks (BFAs)<n>We introduce an analytical framework to study the influence of architectural features, graph properties, and their interaction.<n>We derive theoretical bounds for the number of bit flips required to degrade GNN expressivity on a dataset.
  arXiv  Detail & Related papers  (2025-04-18T16:38:33Z)
• Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data [6.401370088497331]
  This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the interplay between the EEG locations and the semantics of their corresponding brain regions. Our experiments with real-world data demonstrate that NeuroGNN significantly outperforms existing state-of-the-art models.
  arXiv  Detail & Related papers  (2024-05-08T21:36:49Z)
• Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies [39.82681427764513]
  Gated Graph Transformer (GGT) framework designed to predict cognitive metrics based on Functional Connectivity (FC) Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model.
  arXiv  Detail & Related papers  (2024-01-18T19:28:26Z)
• CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis [40.26902764049346]
  We propose a granger causality-inspired graph neural network (CI-GNN) to explain brain-network based psychiatric diagnosis. CI-GNN learns disentangled subgraph-level representations alpha and beta that encode, respectively, the causal and noncausal aspects of original graph. We empirically evaluate the performance of CI-GNN against three baseline GNNs and four state-of-the-art GNN explainers on synthetic data and three large-scale brain disease datasets.
  arXiv  Detail & Related papers  (2023-01-04T14:36:44Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• Generalizing Graph Neural Networks on Out-Of-Distribution Graphs [51.33152272781324]
  Graph Neural Networks (GNNs) are proposed without considering the distribution shifts between training and testing graphs. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation. We propose a general causal representation framework, called StableGNN, to eliminate the impact of spurious correlations.
  arXiv  Detail & Related papers  (2021-11-20T18:57:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.