无需干净脑电数据，用智能分割实现自监督深度去噪。
Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning
- 将输入脑电信号分割为独立噪声版本，共享相同信号源
- 在信噪比低至-10 dB时仍保持优异频谱保真度
- 适合无标注数据的可穿戴脑电设备，尤其适用于零样本场景
可穿戴脑电图（EEG）去噪极具挑战性，因为神经活动微弱且与频谱重叠的噪声伪影难以分离。传统信号处理方法依赖固定或启发式规则，无法应对可穿戴设备中随时间变化的广泛伪影。深度学习方法虽展现出无需分解的去噪潜力，但训练需无伪影脑电数据，而这类数据本质上不可获得。为此，本文提出智能分割自监督去噪（iPSD），通过学习将输入脑电段划分为具有相同底层信号的独立噪声实现，从而在无干净参考的情况下实现深度去噪器的自监督训练，甚至在仅有一个待去噪段的零样本设置下依然有效。我们在多种实验中验证了iPSD，包括来自耳内传感器的可穿戴脑电数据。结果表明，iPSD在极端低信噪比（低至-10 dB）和复杂伪影（如肌电干扰）条件下均达到领先性能，其频谱保真度远超现有基线。
原文摘要 · Abstract (English)
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.
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