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.
arXiv preprint arXiv:2407.20254 , year=
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A unified benchmark across 12 ERP datasets finds that foundation models and deep learning generally outperform traditional manual features for stimulus classification and disease detection, with specific embedding strategies improving Transformer performance.
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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.
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Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models
A unified benchmark across 12 ERP datasets finds that foundation models and deep learning generally outperform traditional manual features for stimulus classification and disease detection, with specific embedding strategies improving Transformer performance.