An improved CapsNet based on data augmentation for driver vigilance estimation with forehead single-channel EEG

• Published in: Cognitive neurodynamics Vol. 18; no. 5; pp. 2535 - 2550
• Main Authors: Yang, Huizhou, Huang, Jingwen, Yu, Yifei, Sun, Zhigang, Zhang, Shouyi, Liu, Yunfei, Liu, Han, Xia, Lijuan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Cognitive Psychology; Computer Science; Correlation coefficients; Data augmentation; Deep learning; EEG; Electroencephalography; Feature maps; Forehead; Fourier transforms; Generative adversarial networks; Integrated approach; Methods; Neurosciences; Physiology; Research Article; Root-mean-square errors; Traffic accidents; Vigilance; Single-channel EEG signals; Cross-subject; Capsule network; Vigilance estimation; CGAN; Self-attention

Various studies have shown that it is necessary to estimate the drivers’ vigilance to reduce the occurrence of traffic accidents. Most existing EEG-based vigilance estimation studies have been performed on intra-subject and multi-channel signals, and these methods are too costly and complicated to i...