ERP-XTTN: Interpretable Prototype-Guided Cross-Attention for Cross-Subject ERP Classification

arXiv:2606.02939 · cs.LG, eess.SP · Submitted 2026-06-01 · Read on arXiv

cs.LG, eess.SP

Submitted: 2026-06-01

Updated: 2026-08-28

Code: https://github.com/stefan-ehrlich/dataset-ErrP-HRI

Terminology

• AUROC — 44× in this paper · explained in 2 episodes
• Core — 35× in this paper · explained in 2 episodes
• Calibration — 21× in this paper · explained in 7 episodes
• Interpretability — 18× in this paper · explained in 15 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Attention Entropy — 6× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Transfer Learning — 4× in this paper · explained in 10 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Signal-to-noise ratio (SNR) — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Necessity of Leave One Subject Out — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Evaluating the structure of cognitive tasks with transfer learning
• A Plug&Play P300 BCI Using Information Geometry

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