GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition
arXiv:2605.25061v1 Announce Type: new 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 propo
The continuous advancements in AI and neuroscience are converging, allowing for more sophisticated interpretations of complex biological data like EEG, which is crucial for real-time diagnostic and therapeutic applications.
This development represents a significant step towards more accurate and objective diagnosis of mood disorders, potentially leading to earlier intervention and personalized treatment strategies based on direct neural information flow.
The shift from statistical correlation to causal influence in GNNs for EEG analysis marks a fundamental change in how neural data is processed, offering deeper insights into brain function and dysfunction.
- · Neuroscience research
- · Psychiatric diagnostics
- · AI/ML medical applications developers
- · Patients with mood disorders
- · Traditional EEG analysis methods relying solely on correlation
- · Subjective diagnostic approaches in mental health
Improved diagnostic accuracy for neurological and psychological conditions based on real-time brain activity.
Development of personalized, AI-driven therapeutic interventions that adapt to individual neural patterns.
Ethical considerations around privacy and misuse of direct neural information for mood and behavioral prediction.
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Read at arXiv cs.LG