Convolutional neural network and riemannian geometry hybrid approach for motor imagery classification. - Chang Gao 003; Wenchao Liu 004; Xian Yang (Neurocomputing, 2022)

Semi-supervised Multi-Source Transfer Learning for Motor Imagery Recognition. - Chang Gao 003; Jie Sun (Int. J. Pattern Recognit. Artif. Intell., 2022)

Human activity recognition through deep learning: Leveraging unique and common feature fusion in wearable multi-sensor systems. - Kang Liu; Chang Gao 003; Binbin Li 002; Wenyuan Liu (Appl. Soft Comput., 2024)

A cross-session motor imagery classification method based on Riemannian geometry and deep domain adaptation. - Wenchao Liu 004; Changjiang Guo; Chang Gao 003 (Expert Syst. Appl., 2024)

UMDA-DDSTGN: An unsupervised meta-domain adaptation method using dynamic directed spatial-temporal graph network for EEG-based emotion recognition. - Chang Gao 003; Lifan Jia; Jiajun Xu; Pengcheng Yang (Knowl. Based Syst., 2025)

Fully connected network samples transfer and multi-classifier fusion for motor imagery recognition. - Sihui Cheng; Chang Gao 003 (Neural Comput. Appl., 2025)