Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning
Authors: Qiyu Rao, Haozhe Tian, Homayoun Hamedmoghadam et al.
Summary
The authors develop a self-supervised method for training deep learning models to denoise EEG signals from wearable sensors, which is hard because you never have truly clean EEG to use as training targets. Their key idea (Intelligent Partitioning for Self-supervised Denoising, iPSD) is to learn how to split a single noisy EEG segment into independent chunks that share the same underlying brain signal but have different noise realizations, then train the denoiser to produce consistent outputs across these partitions. This works even in zero-shot settings where you only have one segment to denoise.
Main takeaways:
- Traditional EEG denoising methods use fixed rules that can't handle time-varying artifacts; deep learning methods need clean training data that doesn't exist
- iPSD eliminates the need for clean reference signals by partitioning a single noisy input into independent realizations with the same underlying signal
- The method enables self-supervised training where the denoiser learns to produce outputs consistent across different noisy views of the same signal
- Works in extremely challenging conditions: signal-to-noise ratios down to -10 dB (signal 10× weaker than noise) and muscle artifacts
- Achieves state-of-the-art performance on wearable in-ear EEG with spectral fidelity orders of magnitude better than baselines
Relevance
No connection to my language model persona research. This is a clever self-supervised learning trick for signal processing, but the domain (EEG denoising) and methods are completely orthogonal to behavioral installation or prompt-to-activation correspondences.
Abstract
arXiv:2605.06724v1 Announce Type: new Abstract: Denoising wearable electroencephalogram (EEG) is inherently challenging since neural activity is not only subtle but also inseparable from spectrally overlapping noise artifacts. Classical signal processing methods, relying on fixed or heuristic rules, cannot handle the time-varying pervasive artifacts in wearable EEGs. Deep learning methods, on the other hand, show promise in decomposition-free EEG denoising using highly expressive neural networks, but the training requires artifact-free EEG, which is inherently unobtainable. To address this, we propose Intelligent Partitioning for Self-supervised Denoising (iPSD). Our method eliminates the need for clean references by learning to partition an input EEG segment into independent noisy realizations with the same underlying signal. This enables self-supervision of deep learning denoisers, even in zero-shot settings where only a single EEG segment to be denoised is available. We validate iPSD through extensive experiments, including validations on wearable EEG from in-ear sensors. The results show that iPSD achieves state-of-the-art performance, most notably under extremely low signal-to-noise ratios (down to -10 dB) and challenging artifacts (e.g., EMG), with spectral fidelity orders of magnitude higher than competitive baselines.