Pilots' Brain Fatigue State Inference Based on Gamma Deep Belief Network
LUO Ying-xue1, JIA Bo2, QIU Xu-yi3, DENG Ping-yu3, REN He4, WU Qi1,3
1. Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China;
2. China Eastern Airlines Technology Application R&D Center Co., Ltd., Shanghai, Shanghai 201700, China;
3. China National Aeronautical Radio Electronics Research Institute, Shanghai 200233, China;
4. COMAC Shanghai Aircraft Customer Service Co., Ltd., Shanghai 200241, China
Abstract：Pilots' fatigue state recognition faces two important issues:how to extract the characteristics that characterize fatigue and how to model fatigue characteristics.Firstly,the EEG(ElectroEncephaloGram) signal is extracted,and the instantaneous frequency domain information based on the affine pseudo-smooth Wigner-Ville distribution is calculated to construct the fatigue state index.Secondly,based on the periodic changes of each channel of EEG signals,the fatigue state classification algorithm of Gamma deep belief network is proposed.Unlike other learning network using convolution and pooling,the proposed network does not split the image or signal,but the hidden layer at the bottom can effectively learn the features of a specific region,and when the number of layers increases,the number of features increases and the features are more general.The Gibbs sampling algorithm for training the deep belief network is improved.The up-down Gibbs sampling is proposed to infer the network parameters.Finally,the experimental results show that the Gamma deep belief network in this paper has achieved satisfactory results in terms of recognition accuracy,stability and iteration time.
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2020, Vol. 48 
