3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 3 cited
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…
cs.CV2025
Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI
Zheng Huang, Enpei Zhang, Weikang Qiu +7
Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to reconstruct visual stimuli, essential…
cs.CE2024★ 1 cited
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…