Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 514 - 526
• Main Authors: Xu, Xueyuan, Wei, Fulin, Jia, Tianyuan, Zhuo, Li, Zhang, Hui, Li, Xiaoguang, Wu, Xia
• Format: Journal Article
• Language: English
• Published: United States The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024; IEEE
• Subjects: Databases, Factual; EEG; Electroencephalogram; Electroencephalography; Electroencephalography - methods; Emotion recognition; Emotions; Feature selection; global relevance; Humans; Labels; multi-dimension emotional labels; Multidimensional methods; Recognition, Psychology
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3355488

Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively.