Dong, Y.;                     Xu, L.;                     Zheng, J.;                     Wu, D.;                     Li, H.;                     Shao, Y.;                     Shi, G.;                     Fu, W.    
        A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet. Brain Sci. 2024, 14, 595.
    https://doi.org/10.3390/brainsci14060595
    AMA Style
    
                                Dong Y,                                 Xu L,                                 Zheng J,                                 Wu D,                                 Li H,                                 Shao Y,                                 Shi G,                                 Fu W.        
                A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet. Brain Sciences. 2024; 14(6):595.
        https://doi.org/10.3390/brainsci14060595
    
    Chicago/Turabian Style
    
                                Dong, Yuefang,                                 Lin Xu,                                 Jian Zheng,                                 Dandan Wu,                                 Huanli Li,                                 Yongcong Shao,                                 Guohua Shi,                                 and Weiwei Fu.        
                2024. "A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet" Brain Sciences 14, no. 6: 595.
        https://doi.org/10.3390/brainsci14060595
    
    APA Style
    
                                Dong, Y.,                                 Xu, L.,                                 Zheng, J.,                                 Wu, D.,                                 Li, H.,                                 Shao, Y.,                                 Shi, G.,                                 & Fu, W.        
        
        (2024). A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet. Brain Sciences, 14(6), 595.
        https://doi.org/10.3390/brainsci14060595