Context-aware tokenization for Cross-subject Emotion Decoding from EEG

arXiv:2606.00884 · cs.LG, cs.AI · Submitted 2026-05-30 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-05-30

Updated: 2026-09-27

Code: https://github.com/jqin4749/Morlet

Terminology

• Transformer — 38× in this paper · explained in 2 episodes
• Accuracy — 27× in this paper · explained in 1 episode
• Tokenization — 26× in this paper · explained in 2 episodes
• Foundation Model — 20× in this paper · explained in 3 episodes
• Foundation Models — 17× in this paper · explained in 7 episodes
• alpha — 14× in this paper · explained in 1 episode
• Redundancy — 11× in this paper · explained in 2 episodes
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Morlet Spectral Transformer — 8× in this paper
• Calibration — 7× in this paper · explained in 7 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Interpretability — 3× in this paper · explained in 15 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Affective Computing — 2× in this paper · explained in 1 episode
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• In International Conference on Learning Representations — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Research Review — 1× in this paper · explained in 6 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Topological Order — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models
• FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
• CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model

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