3 citations · 3 across the 1 of their papers we have counts for
5 papers
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…
What Causes Performance Degradation in Cross-Subject EEG Classification?
Yihe Wang, Taida Li, Yujun Yan +2
Cross-subject Electroencephalography (EEG) classification typically achieves significantly lower performance than subject-dependent settings. Although this phenomenon has been wide…
UnitNorm: Rethinking Normalization for Transformers in Time Series
Nan Huang, Christian Kümmerle, Xiang Zhang
Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and layer normali…
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…