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.
Ahed: A heterogeneous-domain deep learning model for IoT-enabled smart health with few-labeled EEG data.IEEE Internet of Things Journal, 8(23):16787–16800, 2021
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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.