EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

Zhongke Gao; Xinmin Wang; Yuxuan Yang; Chaoxu Mu; Qing Cai; Weidong Dang; Siyang Zuo

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EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

机译：基于EEG的时空卷积神经网络的驾驶员疲劳评估

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摘要

Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial–temporal structure of multichannel electroencephalogram (EEG) signals, we develop a novel E

机译：驾驶员疲劳评估对于交通安全至关重要，许多复杂的因素会加剧这一困难。在本文中，基于多通道脑电图（EEG）信号的时空结构，我们开发了一种新颖的E

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来源
《Neural Networks and Learning Systems, IEEE Transactions on》 |2019年第9期|2755-2763|共9页
作者
Zhongke Gao; Xinmin Wang; Yuxuan Yang; Chaoxu Mu; Qing Cai; Weidong Dang; Siyang Zuo;
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作者单位

School of Electrical and Information Engineering Tianjin University Tianjin China;

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正文语种 eng
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关键词
Electroencephalography; Fatigue; Convolution; Feature extraction; Brain modeling; Task analysis; Physiology;

机译：脑电图;疲劳;卷积;特征提取;大脑建模;任务分析;生理;

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3. EEG-based prediction of driver's cognitive performance by deep convolutional neural network [J] . Hajinoroozi Mehdi, Mao Zijing, Jung Tzyy-Ping, Signal Processing. Image Communication: A Publication of the the European Association for Signal Processing . 2016,第Null期

机译：基于深度脑神经网络的基于EEG的驾驶员认知能力预测
4. A fast 3D convolutional neural networks based spatio-temporal fusion method using spatio -temporal-spectral dataset (STF3DCNN) [C] . Mingyuan Peng, Lifu Zhang, Xuejian Sun, Asian Conference on Remote Sensing . 2020

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5. An Integrative Algorithm/Architecture Co-Design of Deep Spatial and Temporal Separable Convolutional Neural Networks [D] . Baharani, Mohammadreza. 2021

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EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

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