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Emotion Recognition by Learning the Manifold of Fused Multiscale Information of EEG Signals
Cunbo Li;Shuhan Zhang;Yufeng Mu;Lei Yang;Yueheng Peng;Fali Li;Yangsong Zhang;Zhen Liang;Zehong Cao;Feng Wan;Dezhong Yao;Peiyang Li;Peng Xu
IEEE Transactions on Affective Computing
Year: 2025 | Early Access Article |
Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substantial increase in emotion recognition accuracy. However, redundant information within the fused multimodal features could lead to the curse of dimensionality and overfitting of the lear...Show More
Autonomous Driving Agents for Safe Road Junctions Through Deep Reinforcement Learning
2024 IEEE International Conference on Agents (ICA)
Year: 2024 | Conference Paper |
Autonomous driving agents have garnered significant public interest, particularly concerning their safety within transportation systems. Deep reinforcement learning (DRL) has emerged as a promising approach for developing these agents, especially in tasks such as obstacle avoidance. However, agents often operate with partial observations in complex traffic scenarios like road junctions, requiring ...Show More
Nonparametric Dynamic Granger Causality based on Multi-Space Spectrum Fusion for Time-varying Directed Brain Network Construction
Chanlin Yi;Jiamin Zhang;Zihan Weng;Wanjun Chen;Dezhong Yao;Fali Li;Zehong Cao;Peiyang Li;Peng Xu
IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Early Access Article |
Nonparametric estimation of time-varying directed networks can unveil the intricate transient organization of directed brain communication while circumventing constraints imposed by prescribed model-driven methods. A robust time-frequency representation – the foundation of its causality inference – is critical for enhancing its reliability. This study proposed a novel method, i.e., nonparametric d...Show More
Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition
Cunbo Li;Peiyang Li;Zhaojin Chen;Lei Yang;Fali Li;Feng Wan;Zehong Cao;Dezhong Yao;Bao-Liang Lu;Peng Xu
Electroencephalogram (EEG) brain network embodies the brain’s coordination and interaction mechanism, and the transformations of emotional states are usually accompanied with changes in brain network spatial topologies. To effectively characterize emotions, in this work, we propose a cognition-inspired graph embedding model in the L1-norm space (L1-CGE) to learn an optimal low-dimensional embedded...Show More
Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation
IEEE Internet of Things Journal
Cited by: Papers (3)
Traffic oscillations present significant challenges to road transportation systems, resulting in reduced fuel efficiency, heightened crash risks, and severe congestion. Recently emerging Augmented Intelligence of Things (AIoT) technology holds promise for enhancing traffic flow through vehicle-road cooperation. A representative application involves using deep reinforcement learning (DRL) technique...Show More
An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network
Cunbo Li;Tian Tang;Yue Pan;Lei Yang;Shuhan Zhang;Zhaojin Chen;Peiyang Li;Dongrui Gao;Huafu Chen;Fali Li;Dezhong Yao;Zehong Cao;Peng Xu
IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
Cited by: Papers (4)
Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recognition has become one of the hotspots of affective computing. For EEG-based emotion recognition systems, it is crucial to utilize state-of-the-art learning strategies to automatically learn emotion-related brain cognitive patterns from emotional EEG signals, and the learned stable cognitive patterns ...Show More
SATF: A Scalable Attentive Transfer Framework for Efficient Multiagent Reinforcement Learning
IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
Cited by: Papers (1)
It is challenging to train an efficient learning procedure with multiagent reinforcement learning (MARL) when the number of agents increases as the observation space exponentially expands, especially in large-scale multiagent systems. In this article, we proposed a scalable attentive transfer framework (SATF) for efficient MARL, which achieved goals faster and more accurately in homogeneous and he...Show More
Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning
Deep reinforcement learning (DRL) algorithms often face challenges in achieving stability and efficiency due to significant policy gradient variance and inaccurate reward function estimation in complex scenarios. This study addresses these issues in the context of multi-objective car-following control tasks with time lag in traffic oscillations. We propose an expert demonstration reinforcement lea...Show More
A Complex Gaussian Fuzzy Numbers-Based Multisource Information Fusion for Pattern Classification
Shengjia Zhang;Mingrui Yin;Fuyuan Xiao;Zehong Cao;Danilo Pelusi
IEEE Transactions on Fuzzy Systems
Cited by: Papers (5)
Uncertainty modeling and reasoning in intelligent systems are crucial for effective decision-making, such as complex evidence theory (CET) being particularly promising in dynamic information processing. Within CET, the complex basic belief assignment (CBBA) can model uncertainty accurately, while the complex rule of combination can effectively reason uncertainty with multiple sources of informatio...Show More
Fractal Belief Rényi Divergence With its Applications in Pattern Classification
Multisource information fusion is a comprehensive and interdisciplinary subject. Dempster-Shafer (D-S) evidence theory copes with uncertain information effectively. Pattern classification is the core research content of pattern recognition, and multisource information fusion based on D-S evidence theory can be effectively applied to pattern classification problems. However, in D-S evidence theory,...Show More
A Robust Evidential Multisource Data Fusion Approach Based on Cooperative Game Theory and Its Application in EEG
Multisource data fusion analysis, particularly in decision-level fusion strategies, is emerging for application in real-life scenarios. The Dempster–Shafer evidence theory (DSET) is a prevalent approach that has significant importance in managing the fusion tasks. However, existing fusion approaches have limitations in dealing with redundant information and computational complexity associated with...Show More
Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification
Peiyang Li;Xiaohui Gao;Cunbo Li;Chanlin Yi;Weijie Huang;Yajing Si;Fali Li;Zehong Cao;Yin Tian;Peng Xu
Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher i...Show More
Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks
Cunbo Li;Peiyang Li;Yangsong Zhang;Ning Li;Yajing Si;Fali Li;Zehong Cao;Huafu Chen;Badong Chen;Dezhong Yao;Peng Xu
Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with m...Show More
A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making
2023 IEEE International Conference on Unmanned Systems (ICUS)
Year: 2023 | Conference Paper |
Effective target detection and recognition are essential in remote sensing image field, and complex evidence theory (CET) is widely used for this purpose. However, measuring conflict between complex basic belief assignments (CBBAs) in CET is challenging. This study proposes a complex belief Jensen-Shannon divergence based on the complex Pignistic transformation to measure conflict, accounting for ...Show More
Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification
Information can be quantified and expressed by uncertainty, and improving the decision level of uncertain information is vital in modeling and processing uncertain information. Dempster-Shafer evidence theory can model and process uncertain information effectively. However, the Dempster combination rule may provide counter-intuitive results when dealing with highly conflicting information, leading...Show More
The Diagnosis of Major Depressive Disorder Through Wearable fNIRS by Using Wavelet Transform and Parallel-CNN Feature Fusion
Guangming Wang;Ning Wu;Yi Tao;Won Hee Lee;Zehong Cao;Xiangguo Yan;Gang Wang
Depression is a common mental illness that can even lead to suicide in severe cases. Thus, it is essential to diagnose and duly treat the depressive disorder accurately. Functional near-infrared spectroscopy (fNIRS) signals can monitor cerebral hemodynamic activity and may serve as a biomarker of depression. In this study, using wavelet transform and parallel convolutional neural network (CNN) fea...Show More
A Complex Weighted Discounting Multisource Information Fusion With its Application in Pattern Classification
Complex evidence theory (CET) is an effective method for uncertainty reasoning in knowledge-based systems with good interpretability that has recently attracted much attention. However, approaches to improve the performance of uncertainty reasoning in CET-based expert systems remains an open issue. One key to performance improvement is the adequate management of conflict from multisource informati...Show More
Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis
Shiming Chen;Peng Zhang;Guo-Sen Xie;Qinmu Peng;Zehong Cao;Wei Yuan;Xinge You
Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a synthesis model for high-dimensional DT from a small number of training samples. In this article, we p...Show More
Reinforcement Learning From Hierarchical Critics
In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the framework of actor–critic RL, we introduce multiple cooperative critics from two levels of a hierarchy and propose an RL from the hierarchical critics (RLHC) algorithm. In our approach, each agent receives v...Show More
Distributed Semisupervised Fuzzy Regression With Interpolation Consistency Regularization
Ye Shi;Leijie Zhang;Zehong Cao;Mohammad Tanveer;Chin-Teng Lin
IEEE Transactions on Fuzzy Systems
Cited by: Papers (7)
Recently, distributed semisupervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate nonsensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation an...Show More
Jam-Absorption Driving Strategy for Improving Safety Near Oscillations in a Connected Vehicle Environment Considering Consequential Jams
This article proposes an optimal jam-absorption driving (JAD) strategy, which not only prevents the occurring traffic oscillation but also avoids the consequential jam caused by the JAD vehicle. We first conducted a theoretical analysis to estimate the relationship between velocity and space headway within kinematic waves. Then an optimization function was formed to minimize the system travel dela...Show More
A Generalized Deep Neural Network Approach for Digital Watermarking Analysis
Technology advancement has facilitated digital content, such as images, being acquired in large volumes. However, requirement from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a deep neural network (DNN) based watermarking method to achieve this goal. Instead of training a neural network for protecting a specific image...Show More
Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network
Fali Li;Yuqin Li;Hui Zheng;Lin Jiang;Dongrui Gao;Cunbo Li;Yueheng Peng;Zehong Cao;Yangsong Zhang;Dezhong Yao;Tao Xu;Ti-Fei Yuan;Peng Xu
Although the spatiotemporal complexity and network connectivity are clarified to be disrupted during the general anesthesia (GA) induced unconsciousness, it remains to be difficult to exactly monitor the fluctuation of consciousness clinically. In this study, to track the loss of consciousness (LOC) induced by GA, we first developed the multi-channel cross fuzzy entropy method to construct the tim...Show More
Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment
Yu-Cheng Chang;Ye Shi;Anna Dostovalova;Zehong Cao;Jijoong Kim;Daniel Gibbons;Chin-Teng Lin
Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the sca...Show More
Identification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients With Parkinson’s Disease
Zehong Cao;Alka Rachel John;Hsiang-Ting Chen;Kaylena Ehgoetz Martens;Matthew Georgiades;Moran Gilat;Hung T. Nguyen;Simon J. G. Lewis;Chin-Teng Lin
Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between “voluntary stopping” and “involuntary stopping” caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroe...Show More
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