文献类型：期刊文献

英文题名：Recognition of Cognitive Task Load levels using single channel EEG and Stacked Denoising Autoencoder

作者：Yin, Zhong[1]; Zhang, Jianhua[2]

机构：[1] Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; [2] Department of Automation, East China University of Science and Technology, Shanghai, 200237, China

年份：2016

卷号：2016-August

起止页码：3907

外文期刊名：Chinese Control Conference, CCC

收录：EI(收录号：20163802828098)

语种：英文

摘要：Evaluation of operator Cognitive Task Load (CTL) level is quite crucial in Human-Machine (HM) collaborative task environment since operator mental overload or inattention caused by abnormal CTL states may lead to human performance degradation or even catastrophic accidents. One of the most practical approaches tackling this issue is to use ongoing electroencephalogram (EEG) in which human cognitive state can be objectively estimated. However, the accurate recognition of CTL via single channel EEG with the lowest-intrusivity to task condition is particularly challenging as EEG is characterized by individual dependency and nonstationarity. In this paper, a deep learning model designed by Stacked Denoising AutoEncoder (SDAE) is employed on single EEG channel signal to estimate binary levels (low vs. high) of CTL. By adopting a simulated HM process control system, the operator EEG data for 8 healthy subjects under different task demands were collected on two experimental sessions across two consecutive days. Based on the computed full power spectral of EEG. the number of nodes in SDAE is determined by greedy search according to the optimal training error of each layer. The shallow layers of the designed deep network are used to extract the subject-specific information related to CTL variation while the stable power features were reconstructed in those deep layers. Finally, the proposed method is demonstrated to be effective and 74% classification rate across sessions in average of all subjects were achieved. ? 2016 TCCT.