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20242026
most citedEmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition

68 citations · 71 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2026

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding

Shuailei Zhang, Muyun Jiang, Wei Zhang +5

Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology. We p…

cs.LG2025

LEAF: Language-EEG Aligned Foundation Model for Brain-Computer Interfaces

Muyun Jiang, Shuailei Zhang, Zhenjie Yang +9

Recent advances in electroencephalography (EEG) foundation models, which capture transferable EEG representations, have greatly accelerated the development of brain-computer interf…

cs.LG2025

BrainPro: Process-conditioned Pretraining with Topographic Retrieval for Large EEG Models

Yi Ding, Muyun Jiang, Weibang Jiang +6

EEG foundation models are pretrained on heterogeneous corpora, but two challenges remain underexplored: (1) experimental protocols provide mental-process cues that are rarely used…

eess.SP2025

Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer

Muyun Jiang, Yi Ding, Wei Zhang +14

Covert speech involves imagining speaking without audible sound or any movements. Decoding covert speech from electroencephalogram (EEG) is challenging due to a limited understandi…

eess.SP2025★ 3 cited

Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers

Yi Ding, Joon Hei Lee, Shuailei Zhang +2

Learning the spatial topology of electroencephalogram (EEG) channels and their temporal dynamics is crucial for decoding attention states. This paper introduces EEG-PatchFormer, a…

cs.LG2024★ 68 cited

EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition

Yi Ding, Chengxuan Tong, Shuailei Zhang +4

Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial a…