Survey reviews deep learning methods for cross-subject EEG decoding challenges

This survey paper reviews deep learning techniques designed to improve the generalization of electroencephalogram (EEG) decoding across different subjects. It addresses the challenge of high inter-subject variability, which creates a domain shift between training and testing data. The paper categorizes existing methods into families such as feature alignment, adversarial learning, feature disentanglement, and contrastive learning, while also discussing theoretical limitations and the potential of EEG foundation models. AI

IMPACT Provides a structured overview of deep learning approaches for cross-subject EEG decoding, highlighting challenges and future directions like foundation models.

RANK_REASON This is a survey paper on deep learning methods for EEG decoding.

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• adversarial learning
• arXiv
• contrastive learning
• deep learning
• EEG foundation models
• EEG
• feature alignment
• feature disentanglement

• paper
• other

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