Context-aware tokenization for Cross-subject Emotion Decoding from EEG
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 ...
| Authors | Jiaxin Qing, Lexin Li |
|---|---|
| Primary category | cs.LG |
| Published | 30 May 2026 |
| Last updated at source | 27 September 2026 |
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