文献类型：期刊文献

英文题名：An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction

作者：Wang, Zishan[1,2,3];Huang, Ruqiang[3];Yan, Ye[2,3];Luo, Zhiguo[2,3];Zhao, Shaokai[2,3];Wang, Bei[1];Jin, Jing[1];Xie, Liang[2,3];Yin, Erwei[2,3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200030, Peoples R China;[2]Acad Mil Sci AMS, Def Innovat Inst, Beijing 100000, Peoples R China;[3]Tianjin Artificial Intelligence Innovat Ctr TAIIC, Tianjin 300450, Peoples R China

年份：2023

卷号：10

期号：10

外文期刊名：BIOENGINEERING-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001095620000001)】；

基金：This work was supported in part by the grants from the National Natural Science Foundation of China under Grant 62332019, 62076250 and 62176090; in part by STI 2030-major projects 2022ZD0208900; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX; in part by the Program of Introducing Talents of Discipline to Universities through the111 Project under Grant B17017. This research was also supported by National Government Guided-Special Funds for Local Science and Technology Development (Shenzhen, China) (No. 2021Szvup043) and by Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key research and development plan industry foresight and key core technologies) under Grant BE2022064-1.

语种：英文

外文关键词：EEG; IBC; CCA; DE; decision-level fusion

摘要：(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain information of the EEG, as well as bi-channel features that reveal channel-wise relationships across brain regions. Despite these efforts, the mechanism of mutual interactions between EEG rhythms under different emotional expressions remains largely unexplored. Currently, the primary form of information interaction between EEG rhythms is phase-amplitude coupling (PAC), which results in computational complexity and high computational cost. (2) Methods: To address this issue, we proposed a method of extracting inter-bands correlation (IBC) features via canonical correlation analysis (CCA) based on differential entropy (DE) features. This approach eliminates the need for surrogate testing and reduces computational complexity. (3) Results: Our experiments verified the effectiveness of IBC features through several tests, demonstrating that the more correlated features between EEG frequency bands contribute more to emotion classification accuracy. We then fused IBC features and traditional DE features at the decision level, which significantly improved the accuracy of emotion recognition on the SEED dataset and the local CUMULATE dataset compared to using a single feature alone. (4) Conclusions: These findings suggest that IBC features are a promising approach to promoting emotion recognition accuracy. By exploring the mutual interactions between EEG rhythms under different emotional expressions, our method can provide valuable insights into the underlying mechanisms of emotion processing and improve the performance of emotion recognition systems.