no code implementations • 4 Feb 2024 • Youzhi Qu, Chen Wei, Penghui Du, Wenxin Che, Chi Zhang, Wanli Ouyang, Yatao Bian, Feiyang Xu, Bin Hu, Kai Du, Haiyan Wu, Jia Liu, Quanying Liu
During the evolution of large models, performance evaluation is necessarily performed to assess their capabilities and ensure safety before practical application.
no code implementations • 10 Dec 2022 • Wenwei Luo, Wanguang Yin, Quanying Liu, Youzhi Qu
The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms.
no code implementations • 8 Oct 2022 • Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo Wang, Quanying Liu
The results show that STpGCN significantly improves brain decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions.
no code implementations • 7 Jun 2022 • Youzhi Qu, Xinyao Jian, Wenxin Che, Penghui Du, Kai Fu, Quanying Liu
Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning.
no code implementations • 18 May 2021 • Shuhan Zheng, Zhichao Liang, Youzhi Qu, Qingyuan Wu, Haiyan Wu, Quanying Liu
Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN.
no code implementations • 18 Jan 2021 • Wanguang Yin, Youzhi Qu, Zhengming Ma, Quanying Liu
However, most of tensor decomposition methods are the linear feature extraction techniques, which are unable to reveal the nonlinear structure within high-dimensional data.