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eess.SP2024
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models
Mingzhi Chen, Yiyu Gui, Yuqi Su +3
Electroencephalogram (EEG) classification has been widely used in various medical and engineering applications, where it is important for understanding brain function, diagnosing d…
eess.SP2024★ 1 cited
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
Yiyu Gui, MingZhi Chen, Yuqi Su +2
In recent years, with the development of deep learning, electroencephalogram (EEG) classification networks have achieved certain progress. Transformer-based models can perform well…
cs.DC2024
MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Model
Guibo Luo, Hanwen Zhang, Xiuling Wang +2
Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framew…