Cross-subject generalization for EEG decoding: a survey of deep learning methods.

Area of Science:

• Neuroscience
• Machine Learning
• Biomedical Engineering

Background:

• Cross-subject electroencephalography (EEG) decoding is crucial for real-world applications.
• Inter-subject variability presents a significant domain shift, hindering model generalization.
• Existing deep learning models struggle to perform effectively on unseen subjects.

Purpose of the Study:

• To provide a comprehensive review of deep learning methodologies for cross-subject EEG decoding.
• To formalize the cross-subject setting as a multi-source domain problem.
• To establish rigorous, subject-independent evaluation protocols.

Main Methods:

• Systematic literature review and taxonomy of deep learning approaches.
• Categorization into feature alignment, adversarial learning, feature disentanglement, and contrastive learning.
• Formalization of the cross-subject problem as multi-source domain adaptation.

Main Results:

• Identified key deep learning families addressing cross-subject generalization.
• Highlighted the importance of subject-independent evaluation metrics.
• Discussed limitations and future directions in EEG decoding.

Conclusions:

• Deep learning for cross-subject EEG decoding requires specialized methods to overcome domain shift.
• Future advancements depend on addressing theoretical limitations, leveraging subject identity, and exploring EEG foundation models.
• Robust, real-world EEG decoding necessitates improved generalization across diverse subjects.