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.
Lead: Large foundation model for eeg-based alzheimer’s disease detection
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A survey organizes deep learning techniques including feature alignment, adversarial learning, feature disentanglement, and contrastive learning to tackle cross-subject generalization in EEG decoding while formalizing evaluation protocols.
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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.
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Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
A survey organizes deep learning techniques including feature alignment, adversarial learning, feature disentanglement, and contrastive learning to tackle cross-subject generalization in EEG decoding while formalizing evaluation protocols.