Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures

2026-01-13

AI Summary

This work addresses the challenges posed by severe noise contamination in electroencephalography (EEG) signals, the difficulty of modeling their nonlinear dynamical characteristics, and the inherent conflict between denoising and high-level semantic learning objectives. To this end, a two-stage multi-task learning framework is proposed: the first stage employs a denoising autoencoder to suppress artifacts and stabilize temporal dynamics, while the second stage jointly optimizes motor imagery classification, Lyapunov exponent–based chaotic state discrimination, and contrastive representation learning through a shared convolutional-Transformer backbone. By introducing chaos-theoretic labels into EEG multi-task learning for the first time, the method effectively decouples low-level signal restoration from high-level semantic modeling, significantly enhancing model stability, robustness, and generalization. The approach consistently outperforms strong baselines and state-of-the-art methods across multiple EEG datasets.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: State Estimation
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We introduce a two-stage multitask learning framework for analyzing Electroencephalography (EEG) signals that integrates denoising, dynamical modeling, and representation learning. In the first stage, a denoising autoencoder is trained to suppress artifacts and stabilize temporal dynamics, providing robust signal representations. In the second stage, a multitask architecture processes these denoised signals to achieve three objectives: motor imagery classification, chaotic versus non-chaotic regime discrimination using Lyapunov exponent-based labels, and self-supervised contrastive representation learning with NT-Xent loss. A convolutional backbone combined with a Transformer encoder captures spatial-temporal structure, while the dynamical task encourages sensitivity to nonlinear brain dynamics. This staged design mitigates interference between reconstruction and discriminative goals, improves stability across datasets, and supports reproducible training by clearly separating noise reduction from higher-level feature learning. Empirical studies show that our framework not only enhances robustness and generalization but also surpasses strong baselines and recent state-of-the-art methods in EEG decoding, highlighting the effectiveness of combining denoising, dynamical features, and self-supervised learning.

Problem

• EEG
• noise
• nonlinear dynamics
• representation learning
• motor imagery

Innovation

• multitask learning
• contrastive learning
• nonlinear dynamics
• denoising autoencoder
• EEG decoding