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Peiyang Li
Researcher at University of Electronic Science and Technology of China
Publications - 43
Citations - 1320
Peiyang Li is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Computer science & Electroencephalography. The author has an hindex of 17, co-authored 30 publications receiving 852 citations. Previous affiliations of Peiyang Li include Chongqing University of Posts and Telecommunications.
Papers
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Journal ArticleDOI
EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations
Peiyang Li,Huan Liu,Yajing Si,Cunbo Li,Fali Li,Xuyang Zhu,Xiaoye Huang,Ying Zeng,Dezhong Yao,Yangsong Zhang,Peng Xu +10 more
TL;DR: Both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition to develop the effective human–computer interaction systems by adapting to human emotions in the real world applications.
Journal ArticleDOI
Efficient resting-state EEG network facilitates motor imagery performance.
Rui Zhang,Dezhong Yao,Pedro A. Valdes-Sosa,Pedro A. Valdes-Sosa,Fali Li,Peiyang Li,Tao Zhang,Teng Ma,Yongjie Li,Peng Xu +9 more
TL;DR: The network mechanisms of the MI-BCI are revealed and may help to find new strategies for improving MI- BCI performance and may be a source of inspiration for practical BCI applications beyond the laboratory.
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Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network.
Tao Zhang,Tiejun Liu,Fali Li,Mengchen Li,Dongbo Liu,Rui Zhang,Hui He,Peiyang Li,Jinnan Gong,Cheng Luo,Dezhong Yao,Peng Xu +11 more
TL;DR: This study recorded multi-modal datasets consisting of MI electroencephalography signals, T1-weighted structural and resting-state functional MRI data for each subject, and a correlation analysis was used to elucidate the relationships between the fronto-parietal attention network (FPAN) and MI-BCI performance.
Journal ArticleDOI
The Time-Varying Networks in P300: A Task-Evoked EEG Study
Fali Li,Bei Chen,He Li,Tao Zhang,Fei Wang,Yi Jiang,Peiyang Li,Teng Ma,Rui Zhang,Yin Tian,Tiejun Liu,Daqing Guo,Dezhong Yao,Peng Xu +13 more
TL;DR: Investigating the time-varying information processing in P300 found that different stages of P300 evoked different brain networks, indicating that the two brain hemispheres exhibit asymmetrical functions in processing related information for different P300 stages.
Journal ArticleDOI
The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential.
Teng Ma,Hui Li,Lili Deng,Hao Yang,Xulin Lv,Peiyang Li,Fali Li,Rui Zhang,Tiejun Liu,Dezhong Yao,Peng Xu +10 more
TL;DR: The offline analysis demonstrates that the hybrid BCI system proposed in this paper could evoke the desired MI and mVEP signal features simultaneously, and both are very close to those evoked in the single-modality BCI task.