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Found 2 papers, 1 papers with code
Data augmentation for learning predictive models on EEG: a systematic comparison
Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Data augmentation is a key element of deep learning pipelines, as it informs the network during training about transformations of the input data that keep the label unchanged.
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