Data augmentation for learning predictive models on EEG: a systematic comparison
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/aca220
Abstract: Objective. The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation,… read more here.
Keywords: predictive models; augmentation learning; data augmentation; models eeg ... See more keywords
Consistent Augmentation Learning for Generalizing CLIP to Unseen Domains
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3479691
Abstract: Domain generalization (DG) is a challenging transfer learning task focused on learning invariant knowledge from limited source domains, thereby enhancing generalization to the out-of-distribution data in unseen domains. Recent advancements in vision-language models (VLMs) have… read more here.
Keywords: unseen domains; augmentation learning; consistent augmentation; clip ... See more keywords
Contrastive Hierarchical Augmentation Learning for Modeling Cognitive and Multimodal Brain Network
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Computational Social Systems"
DOI: 10.1109/tcss.2024.3402328
Abstract: Brain networks generated by functional magnetic resonance imaging (fMRI) have shown promising performance in characterizing cerebral social cognition and disorders. However, the scarcity of labeled data has hindered the application of deep graph learning in… read more here.
Keywords: brain; augmentation learning; network; hierarchical augmentation ... See more keywords