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]; Huang, Ruqiang[1,3]; Zhang, Lei[1,2]; Zhao, Shaokai[1,3]; Wang, Bei[2]; Jin, Jing[2]; Yan, Ye[1,3]; Yin, Erwei[1,3]

机构：[1] Tianjin Artificial Intelligence Innovation Center [TAIIC], Tianjin, China; [2] East China University of Science and Technology, Shanghai, China; [3] Defense Innovation Institute, Academy of Military Sciences [AMS], Beijing, China

年份：2024

卷号：103

起止页码：273

外文期刊名：IFMBE Proceedings

收录：EI(收录号：20241916040009)

语种：英文

外文关键词：Brain - Correlation methods - Electroencephalography - Frequency domain analysis - Speech recognition - Time domain analysis

摘要：As the most active and promising research fields of affective computing, emotion recognition based on EEG signal has attracted much attention. Traditional methods usually pay their attention on single-channel features which reflect time-domain, or frequency-domain of EEG and bi-channel features which reflecting channel-wise relationship across brain regions. However, emotional features which capturing the coupling between the EEG frequency bands was seldom to discuss. In this paper, we proposed a method to extract the inter-bands correlation (IBC) features based on canonical correlation analysis (CCA). Firstly, we verified the validity of the IBC features through several experiments and found that the more correlated features between the EEG frequency bands contribute more to emotion classification. Then, the IBC features and traditional differential entropy (DE) were fused at the decision-level, which significantly improves the accuracy of emotion recognition on SEED dataset and local CUMULATE dataset. Our results show that IBC features is a promising method to promote the emotion recognition accuracy. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.