Cross-subject Drowsiness Recognition Model Based on the Multi-scale Parallel Convolution of EEG Channels
文献类型： 会议论文
第一作者： Shaohua Wan
作者： Shaohua Wan 1 ; Songquan Li 2 ; Rongbo Zhu 2 ; Jun Yuan 3 ;
作者机构： 1.Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
2.College of Informatics, Huazhong Agricultural University, Wuhan, China|Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China|Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
3.College of Informatics, Huazhong Agricultural University, Wuhan, China
关键词： Deep learning;Electric potential;Correlation;Convolution;Feature extraction;Brain modeling;Electroencephalography
会议名称： Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking
主办单位：
页码： 709-714
摘要： Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drowsiness, making it difficult to extract shared features from differentiated subjects. This paper proposes a novel deep learning model called CPCNN to address the challenging of cross-subject drowsiness recognition without calibration and improve accuracy. CPCNN obtains delta frequency band features related to drowsiness from sub-features in the channel dimension, effectively combining the advantages of delta frequency bands in EEG with the richness of channel features. To capture the common features of drowsiness, a channel slicing module is proposed, which divides the spatial and channel features extracted by the deep separable convolution module into multiple sub-features along the channel dimension. In addition, to further improve cross-subject recognition performance, a multi-scale parallel group convolution is designed to capture four key frequency sub-feature groups within the delta frequency band. The experimental results show that CPCNN boosts the recognition performance, significantly outperforms all strong baselines. On balanced and unbalanced public datasets, the average accuracy of CPCNN is 81.49% and 78.26%, respectively, surpassing the excellent baseline method by 3.14% and 0.56%.
分类号： tp3-53
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