no code implementations • 31 Jan 2024 • Song Wang, Chen Wei, Kexin Lou, Dongfeng Gu, Quanying Liu
Here, we present a novel method which utilizes the Brain Geometric-informed Basis Functions (GBFs) as priors to enhance EEG/MEG source imaging.
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 • 10 Nov 2022 • Mo Wang, Kexin Lou, Zeming Liu, Pengfei Wei, Quanying Liu
In this paper, we propose a general framework called multi-objective optimization via evolutionary algorithms (MOVEA) to address the non-convex optimization problem in designing TES strategies without predefined direction.
1 code implementation • 2 Dec 2021 • Junjie Yu, Chenyi Li, Kexin Lou, Chen Wei, Quanying Liu
DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal.
no code implementations • 18 Feb 2021 • Chen Wei, Kexin Lou, Zhengyang Wang, Mingqi Zhao, Dante Mantini, Quanying Liu
EEG source localization is an important technical issue in EEG analysis.