1 code implementation • 21 Sep 2023 • Qi Wang, Li Chen, Zhiyuan Zhan, Jianhua Zhang, Zhong Yin
This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets.
no code implementations • 16 Nov 2022 • Zhe Wang, Yongxiong Wang, Chuanfei Hu, Zhong Yin, Yu Song
Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition.
no code implementations • 19 Aug 2022 • Yiwen Zhu, Kaiyu Gan, Zhong Yin
In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG patterns based on the graph attention mechanism.
no code implementations • 14 Apr 2021 • Chunhua Ye, Zhong Yin, Chenxi Wu, Xiayidai Abulaiti, Yixing Zhang, Zhenqi Sun, Jianhua Zhang
The combination of Frequency and entropy feature and CNN has the highest classification accuracy, which is 85. 34%.
no code implementations • 26 Sep 2020 • Xiaolong Zhong, Zhong Yin
Use of the electroencephalogram (EEG) and machine learning approaches to recognize emotions can facilitate affective human computer interactions.