Browsing ODA Open Digital Archive by Author "Yin, Zhong"
Now showing items 1-9 of 9
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Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders
Yang, Shuo; Yin, Zhong; Wang, Yagang; Zhang, Wei; Wang, Yongxiong; Zhang, Jianhua (Computers in Biology and Medicine;Volume 109, June 2019, Journal article; Peer reviewed, 2019-04-26)To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing ... -
Cross-Subject emotion recognition from EEG using Convolutional Neural Networks
Zhong, Xiaolong; Yin, Zhong; Zhang, Jianhua (Chinese Control Conference (CCC);2020 39th Chinese Control Conference (CCC), Peer reviewed; Journal article, 2020-09-09)Using electroencephalogram (EEG) signals for emotion detection has aroused widespread research concern. However, across subjects emotional recognition has become an insurmountable gap which researchers cannot step across ... -
EEG-based affect classification with machine learning algorithms
Zhang, Jianhua; Yin, Zhong; Chen, Peng (Chapter, 2023)In this paper, we aim to study the EEG-based emotion recognition problem. First, we use clustering algorithm to determine the target class of emotions and perform binary classification of emotion along its arousal and ... -
An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals
Zheng, Zhanpeng; Yin, Zhong; Zhang, Jianhua (Chinese Control Conference (CCC);2020 39th Chinese Control Conference (CCC), Conference object, 2020-09-09)Evaluating operator cognitive workload (CW) levels in human-machine systems based on neurophysiological signals is becoming the basis to prevent serious accidents due to abnormal state of human operators. This study proposes ... -
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
Zhang, Jianhua; Yin, Zhong; Chen, Peng; Nichele, Stefano (Information Fusion;Volume 59, July 2020, Peer reviewed; Journal article, 2020-01-31)In recent years, the rapid advances in machine learning (ML) and information fusion has made it possible to endow machines/computers with the ability of emotion understanding, recognition, and analysis. Emotion recognition ... -
Manifold feature fusion with dynamical feature selection for cross-subject emotion recognition
Hua, Yue; Zhong, Xiaolong; Zhang, Bingxue; Yin, Zhong; Zhang, Jianhua (Brain Sciences;Volume 11 / Issue 11, Peer reviewed; Journal article, 2021-10-23)Affective computing systems can decode cortical activities to facilitate emotional human– computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer ... -
Operator Performance Prediction based on Fuzzy Modeling Approach
Zhang, Jianhua; Yin, Zhong (IFAC-PapersOnLine;Volume 53, Issue 2, 2020, Peer reviewed; Journal article, 2021-04-14)In this paper, physiological signals were measured from five participants, each participating in two sessions of experiment with identical experimental procedure. A simulation platform, AutoCAMS (Automation-enhanced Cabin ... -
Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework
Yin, Zhong; Zhao, Mengyuan; Zhang, Wei; Wang, Yongxiong; Wang, Yagang; Zhang, Jianhua (Neurocomputing;Volume 347, 28 June 2019, Journal article; Peer reviewed, 2019-02-19)Evaluating operator mental workload (MW) in human-machine systems via neurophysiological signals is crucial for preventing unpredicted operator performance degradation. However, the feature of physiological signals is ... -
Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features
Cao, Zixuan; Yin, Zhong; Zhang, Jianhua (Peer reviewed; Journal article, 2020)