DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding

2025-06-26

AI Summary

To address the limited receptive field of CNNs in EEG decoding—hindering modeling of long-range temporal dependencies and global inter-channel relationships—this paper proposes a dual-branch convolutional-Transformer architecture. One branch employs a temporal Conformer to capture long-range temporal dynamics; the other integrates a spatial Conformer with a lightweight channel-wise attention module to jointly model cross-channel spatial interactions and physiological relevance. This design explicitly combines local convolutional inductive biases with global self-attention, balancing computational efficiency and interpretability. Evaluated on five motor imagery and two epilepsy detection datasets, the model consistently outperforms ten state-of-the-art methods, using fewer than 1/8 the parameters of EEG-Conformer. Moreover, feature visualizations align with prior neurophysiological knowledge of sensorimotor cortex activation patterns.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolutional neural networks (CNNs) remain the mainstream backbone for EEG decoding, their inherently short receptive field makes it difficult to capture long-range temporal dependencies and global inter-channel relationships. Recent CNN-Transformer (Conformers) hybrids partially address this issue, but most adopt a serial design, resulting in suboptimal integration of local and global features, and often overlook explicit channel-wise modeling. To address these limitations, we propose DBConformer, a dual-branch convolutional Transformer network tailored for EEG decoding. It integrates a temporal Conformer to model long-range temporal dependencies and a spatial Conformer to extract inter-channel interactions, capturing both temporal dynamics and spatial patterns in EEG signals. A lightweight channel attention module further refines spatial representations by assigning data-driven importance to EEG channels. Extensive experiments on five motor imagery (MI) datasets and two seizure detection datasets under three evaluation settings demonstrate that DBConformer consistently outperforms 10 competitive baseline models, with over eight times fewer parameters than the high-capacity EEG Conformer baseline. Further, the visualization results confirm that the features extracted by DBConformer are physiologically interpretable and aligned with sensorimotor priors in MI. The superior performance and interpretability of DBConformer make it reliable for robust and explainable EEG decoding. Code is publicized at https://github.com/wzwvv/DBConformer.

Problem

• Improving EEG decoding by capturing long-range temporal dependencies
• Enhancing global inter-channel relationships in EEG signal analysis
• Integrating local and global features for robust EEG interpretation

Innovation

• Dual-branch network for EEG decoding
• Temporal and spatial Conformer integration
• Lightweight channel attention module