no code implementations • 22 Feb 2024 • Xinke Shen, Lingyi Tao, Xuyang Chen, Sen Song, Quanying Liu, Dan Zhang
Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a general framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER).
no code implementations • 19 Jul 2023 • Peizhen Yang, Xinke Shen, Zongsheng Li, Zixiang Luo, Kexin Lou, Quanying Liu
Specifically, we trained neural networks (i. e., CNN, vanilla RNN, GRU, LSTM, and Transformer) to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity (EC) between the perturbed EEG channel and the rest of the channels.
no code implementations • 20 Sep 2021 • Xinke Shen, Xianggen Liu, Xin Hu, Dan Zhang, Sen Song
Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signal representations across subjects when they received the same emotional stimuli in contrast to different ones.