iPSD enables self-supervised training of deep EEG denoisers by learning to partition noisy segments into independent noisy realizations of the same neural activity, achieving state-of-the-art performance at very low SNR without clean references.
CHB-MIT Scalp EEG Database.PhysioNet, June 2010
2 Pith papers cite this work. Polarity classification is still indexing.
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Sleep-only contrastive pretraining improves results on non-sleep EEG and ECG tasks relative to training from scratch and matches or exceeds some specialized models.
citing papers explorer
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Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning
iPSD enables self-supervised training of deep EEG denoisers by learning to partition noisy segments into independent noisy realizations of the same neural activity, achieving state-of-the-art performance at very low SNR without clean references.
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Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
Sleep-only contrastive pretraining improves results on non-sleep EEG and ECG tasks relative to training from scratch and matches or exceeds some specialized models.