From the 1 of 4 linked papers with an AI index.
2 citations · 3 across the 2 of their papers we have counts for
4 papers
What Causes Performance Degradation in Cross-Subject EEG Classification?
Yihe Wang, Taida Li, Yujun Yan +2
The paper systematically studies why cross‑subject EEG classification performs worse than subject‑dependent classification, identifying inter‑subject variability and shortcut learn…
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
Taida Li, Yujun Yan, Fei Dou +2
Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This s…
Repurposing Foundation Model for Generalizable Medical Time Series Classification
Nan Huang, Haishuai Wang, Zihuai He +2
Medical time series (MedTS) classification suffers from poor generalizability in real-world deployment due to inter- and intra-dataset heterogeneity, such as varying numbers of cha…
Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
Yihe Wang, Nan Huang, Taida Li +2
Medical time series (MedTS) data, such as Electroencephalography (EEG) and Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases…