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[2601.08549] Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures
Summary
This article presents a two-stage multitask learning framework for analyzing EEG signals, focusing on denoising and representation learning to enhance robustness in brain signal classification.
Why It Matters
The research addresses the challenges of noise in EEG signal analysis, which is critical for applications in neuroscience and brain-computer interfaces. By improving the accuracy of signal interpretation, this work could lead to advancements in medical diagnostics and cognitive research.
Key Takeaways
- Introduces a two-stage framework combining denoising and multitask learning for EEG signals.
- Utilizes a denoising autoencoder to enhance signal stability and representation.
- Achieves multiple objectives, including motor imagery classification and chaotic regime discrimination.
- Demonstrates improved robustness and generalization over existing methods.
- Highlights the effectiveness of integrating nonlinear dynamical features in EEG analysis.
Computer Science > Machine Learning arXiv:2601.08549 (cs) [Submitted on 13 Jan 2026 (v1), last revised 23 Feb 2026 (this version, v3)] Title:Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures Authors:Sucheta Ghosh, Felix Dietrich, Zahra Monfared View a PDF of the paper titled Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures, by Sucheta Ghosh and Felix Dietrich and Zahra Monfared View PDF HTML (experimental) 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 separat...