通过分阶段学习提升脑电图噪声下的解码能力
Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures
- 先去噪再多任务学习，分离降噪与特征提取
- 在多个数据集上超越现有方法，提升解码精度
- 适合脑机接口、神经动力学分析等研究者
我们提出一种两阶段多任务学习框架，用于分析带有噪声的脑电图（EEG）信号，融合去噪、动力学建模和表征学习。第一阶段训练去噪自编码器以抑制伪迹并稳定时序动态，提供鲁棒的信号表示；第二阶段采用多任务架构处理去噪后的信号，实现三个目标：运动想象分类、基于李雅普诺夫指数标签的混沌与非混沌状态判别，以及使用NT-Xent损失的自监督对比表征学习。模型采用卷积主干与Transformer编码器，捕捉时空结构，动力学任务增强对非线性脑动力学的敏感性。该分阶段设计减少重建与判别目标间的干扰，提升跨数据集稳定性，支持可复现训练。实证研究表明，该框架不仅增强鲁棒性与泛化能力，还在脑电信号解码中超越强基线与最新方法，验证了去噪、动力学特征与自监督学习结合的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。