CNN-Transformer network for student learning effect prediction using EEG signals based on spatio-temporal feature fusion

Hui Xie, Zexiao Dong, Huiting Yang, Yanxia Luo, Shenghan Ren, Pengyuan Zhang, Jiangshan He, Chunli Jia, Yuqiang Yang, Mingzhe Jiang, Xinbo Gao, Xueli Chen

Abstract: Highlights•We propose an experiment protocol that simulates online learning in real study life.•HCT-learn can predict learning performance from the EEG in the learning process.•Fusing spatial and temporal whole-brain EEG reached 90% LOSO accuracy within-dataset.•Grad-CAM visualizations reveal spatial-spectral neural patterns during learning.