EEG-based prediction of driver's cognitive performance by deep convolutional neural network
EEG-based prediction of driver's cognitive performance by deep convolutional neural network
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DOI：
10.1016/j.image.2016.05.018
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发表时间：
2016-09-01
影响因子：
3.5
通讯作者：
Huang, Yufei
中科院分区：
文献类型：
--
作者：
Hajinoroozi, Mehdi;Mao, Zijing;Huang, Yufei
我们考虑使用脑电图信号来预测与驾驶表现相关的驾驶员认知状态。我们提出了一种新颖的逐通道卷积神经网络（CCNN），其架构考虑了脑电图数据的独特特征。我们还讨论了 CCNN-R，这是一种使用受限玻尔兹曼机代替卷积滤波器的 CCNN 变体，并推导了详细的算法。为了测试 CCNN 和 CCNN-R 的性能，我们从 3 项驾驶疲劳研究中收集了一个大型 EEG 数据集，其中包括来自 37 名受试者的样本。使用该数据集，我们研究了原始 EEG 数据上的新 CCNN 和 CCNN-R 以及独立成分分析 (ICA) 分解。我们测试了受试者内和跨受试者预测，结果表明 CCNN 和 CCNN-R 比传统 DNN 和 CNN 以及其他非 DL 算法实现了稳健且改进的性能。 (C) 2016 Elsevier B.V. 保留所有权利。
We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms. (C) 2016 Elsevier B.V. All rights reserved.