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.
1 code implementation • 31 Dec 2022 • Zixiang Luo, Kaining Peng, Zhichao Liang, Shengyuan Cai, Chenyu Xu, Dan Li, Yu Hu, Changsong Zhou, Quanying Liu
Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain.
no code implementations • 23 Oct 2022 • Wanyi Zhuang, Qi Chu, Zhentao Tan, Qiankun Liu, Haojie Yuan, Changtao Miao, Zixiang Luo, Nenghai Yu
UPCL is designed for learning the consistency-related representation with progressive optimized pseudo annotations.
no code implementations • 26 Mar 2021 • Zhichao Liang, Zixiang Luo, Keyin Liu, Jingwei Qiu, Quanying Liu
In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies.