MI-DGHCL: Motor imagery EEG domain generalization via hyperbolic contrastive learning

Key Points

• Findings show a notable increase in classification accuracy for motor imagery tasks, suggesting effectiveness of the model.
• The accuracy improvement reached up to 15% across generalization scenarios, emphasizing the model's robustness.
• Assessment using hyperbolic contrastive learning demonstrates potential in EEG signal processing and motor imagery classification.
• Highlights the need for further exploration of hyperbolic spaces in neuroscience applications for broader impact.