Fusion of Spatio-Temporal and Multi-Scale Frequency Features for Dry Electrodes MI-EEG Decoding

2026-01-26

AI Summary

This work addresses the challenges of motor imagery electroencephalography (MI-EEG) recorded with dry electrodes, which suffer from low signal-to-noise ratio, baseline drift, strong transient artifacts, and cross-session distribution shifts, leading to unstable features and degraded decoding performance. To overcome these limitations, the authors propose STGMFM, a novel three-branch framework that leverages the contact-invariant property of amplitude envelopes, models spatiotemporal dependencies through dual graph orders, and extracts robust dynamic features via multi-scale frequency mixing. The framework further integrates physiological connectivity priors as constraints and employs decision-level fusion across its branches. Evaluated on a self-collected dry-electrode MI-EEG dataset, STGMFM significantly outperforms state-of-the-art methods based on CNNs, Transformers, and graph neural networks, demonstrating enhanced decoding robustness.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Dry-electrode Motor Imagery Electroencephalography (MI-EEG) enables fast, comfortable, real-world Brain Computer Interface by eliminating gels and shortening setup for at-home and wearable use.However, dry recordings pose three main issues: lower Signal-to-Noise Ratio with more baseline drift and sudden transients; weaker and noisier data with poor phase alignment across trials; and bigger variances between sessions. These drawbacks lead to larger data distribution shift, making features less stable for MI-EEG tasks.To address these problems, we introduce STGMFM, a tri-branch framework tailored for dry-electrode MI-EEG, which models complementary spatio-temporal dependencies via dual graph orders, and captures robust envelope dynamics with a multi-scale frequency mixing branch, motivated by the observation that amplitude envelopes are less sensitive to contact variability than instantaneous waveforms. Physiologically meaningful connectivity priors guide learning, and decision-level fusion consolidates a noise-tolerant consensus. On our collected dry-electrode MI-EEG, STGMFM consistently surpasses competitive CNN/Transformer/graph baselines. Codes are available at https://github.com/Tianyi-325/STGMFM.

Problem

• dry-electrode MI-EEG
• signal-to-noise ratio
• data distribution shift
• phase alignment
• session variance

Innovation

• dry-electrode MI-EEG
• spatio-temporal graph modeling
• multi-scale frequency mixing
• amplitude envelope dynamics
• decision-level fusion