2 citations · 3 across the 9 of their papers we have counts for
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HCFT: Hierarchical Convolutional Fusion Transformer for EEG Decoding
Haodong Zhang, Jiapeng Zhu, Yitong Chen +1
Electroencephalography (EEG) decoding requires models that can effectively extract and integrate complex temporal, spectral, and spatial features from multichannel signals. To addr…
DRDCAE-STGNN: An End-to-End Discrimina-tive Autoencoder with Spatio-Temporal Graph Learning for Motor Imagery Classification
Yi Wang, Haodong Zhang, Hongqi Li
Motor imagery (MI) based brain-computer interfaces (BCIs) hold significant potential for assistive technologies and neurorehabilitation. However, the precise and efficient decoding…
Foundation Models for Cross-Domain EEG Analysis Application: A Survey
Hongqi Li, Yitong Chen, Yujuan Wang +2
Electroencephalography (EEG) analysis stands at the forefront of neuroscience and artificial intelligence research, where foundation models are reshaping the traditional EEG analys…
Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding
Hongqi Li, Haodong Zhang, Yitong Chen
The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To eff…