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.
A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals.Neurocomputing, 404:108–121, 2020
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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.