BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal

2026-04-27

AI Summary

This study addresses the challenge of electroencephalogram (EEG) signal contamination by ocular (EOG) and muscular (EMG) artifacts, which compromises the reliability of neural diagnostics and brain–computer interfaces. To this end, the authors propose an adaptive band-routing neural network that decomposes EEG signals into frequency bands and employs a spatiotemporal adaptive routing mechanism to dynamically modulate denoising intensity across time points within each band. A full-band conditioning module is further integrated to enable global contextual modulation and coarse-grained reconstruction. The resulting lightweight, end-to-end model contains only 0.2 million parameters and achieves state-of-the-art performance on the EEGDenoiseNet benchmark, demonstrating superior reductions in relative root mean square error (RRMSE) and greater improvements in signal-to-noise ratio (SNR) for EOG, EMG, and mixed artifacts compared to existing methods.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Online Learning & Bandits
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Electroencephalography (EEG) is highly susceptible to artifact contamination, such as electrooculographic (EOG) and electromyographic (EMG) interference, which severely degrades signal quality and hinders reliable interpretation in applications including neurological diagnosis, brain-computer interfaces (BCIs), etc. Effective EEG denoising remains challenging because different artifact sources exhibit diverse and temporally varying distributions, together with distinct spectral characteristics across frequency bands. To address these issues, we propose BandRouteNet, an adaptive frequency-aware neural network for EEG denoising that jointly exploits band-specific processing and full-band contextual modeling. The proposed model performs band-wise denoising to explicitly capture frequency-dependent artifact patterns. Within this framework, we introduce a routing mechanism that adaptively determines where and to what extent denoising should be applied across temporal locations within each frequency band. In parallel, a full-band conditioner directly processes the original noisy EEG to extract global temporal context, producing both conditional parameters for modulating the band-wise pathway and a coarse-grained signal-level refinement to supplement the final reconstruction. Extensive experiments on the EEGDenoiseNet benchmark dataset demonstrate that BandRouteNet outperforms other methods under EOG, EMG, and mixed-artifact conditions in terms of Relative Root Mean Square Error (RRMSE) and Signal-to-Noise Ratio Improvement (SNR$_{\text{imp}}$) under unified experimental settings, while remaining highly parameter-efficient with only 0.2M trainable parameters. These results highlight its strong potential for high-performance EEG artifact removal in resource-constrained applications.

Problem

• EEG artifact removal
• electrooculographic interference
• electromyographic interference
• signal denoising
• frequency bands

Innovation

• adaptive band routing
• frequency-aware denoising
• EEG artifact removal
• band-wise processing
• full-band conditioning