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

arXiv: https://arxiv.org/abs/2606.00884 · HTML: https://mlsanity.com/paper/2606.00884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 30 May 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models](https://mlsanity.com/paper/2409.04701/) (2024)
- [FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface](https://mlsanity.com/paper/2104.01233/) (2021)
- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2406.18345/) (2024)
- [Contextual Document Embeddings](https://mlsanity.com/paper/2410.02525/) (2024)
- [Tokenizing Single-Channel EEG with Time-Frequency Motif Learning](https://mlsanity.com/paper/2502.16060/) (2025)
- [EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models](https://mlsanity.com/paper/2601.17883/) (2026)

## Abstract (arXiv)

In cross-subject EEG emotion decoding, neighboring windows can inform the representation of a target window, but they contain both background variation and sustained task signals. This creates a challenge for contextual representation learning because subtracting shared activity can also remove useful information. We introduce the Morlet Spectral Transformer (MST), which conditions target-window tokens on a structured spectral summary before attention. MST averages the Morlet log-amplitude spectra of unlabeled same-trial neighbors and uses frequency-specific electrode projections to combine the target spectrum with its reference-relative residual. This retains access to absolute activity while incorporating context into a fixed attended target-token grid. Without external pretraining, MST achieves 66.5%, 40.7%, 36.1%, and 27.9% accuracy on SEED, SEED-IV, SEED-V, and SEED-VII under leave-one-subject-out evaluation, averaged over three random seeds, outperforming the evaluated pretrained and from-scratch baselines. On FACED, MST achieves 17.52% accuracy in nine-class cross-subject evaluation, exceeding the strongest evaluated baseline by 1.15 percentage points. We also performed the ablation studies across all 15 SEED subjects to evaluate the contribution of different components of MST.