文献类型：期刊文献

英文题名：Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks

作者：Zhang, Jianhua[1];Li, Sunan[1];Wang, Rubin[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Sch Sci, Shanghai, Peoples R China

年份：2017

卷号：11

外文期刊名：FRONTIERS IN NEUROSCIENCE

收录：;WOS:【SSCI(收录号:WOS:000406530200003)，SCI-EXPANDED(收录号:WOS:000406530200003)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant No. 61075070 and Key Grant No. 11232005.

语种：英文

外文关键词：mental workload; pattern classification; convolutional neural network; ensemble learning; deep learning; electrophysiology

摘要：In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.