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A Pilot Study on Coupling Behaviors of Limb Movements and Brain Activation Based on EEG and fNIRS Synchronization Information

A Pilot Study on Coupling Behaviors of Limb Movements and Br...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Tang, Xi Li, Qingge Tang, Yao Tian, Lan Zheng, Yue Li, Xiangxin Jiang, Naifu Shang, Peng Li, Guanglin Peng, Liang Fang, Peng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems The Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350327182

Motion is one of the basic physiological functions of human beings. However, many brain diseases such as stroke may cause different degrees of motor dysfunctions for patients. As a commonly used rehabilitation method, active and passive exercise training may enhance patients' neuromuscular functions and recover their motor abilities. It is known that limb movements are strongly coupled with brain activation but there is currently insufficient exploration on the coupling behaviors from the perspective of informatics. In this study, the coupling relationship between limb movements and brain activation was preliminarily studied based on three healthy subjects. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals were synchronously collected during lower limb movements, and time-frequency analysis (TFA) and transfer entropy (TE) analysis were performed to quantitatively study the brain activation behaviors. In the experiments, a desynchronization phenomenon of μ rhythm in EEG was observed during exercise states, and the experimental results demonstrate the activation rule of motor and prefrontal cortexes upon limb movements. Calculations show that there exists a bidirectional flow of information between EEG and cerebral oxygen metabolism signals, but with a difference between different directions. This work may support the rehabilitation for patients with motor dysfunctions with a guidance of quantitative indicators and also benefit the exploration on neuroscience. © 2023 IEEE.

关键词： Electroencephalography

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Evaluation of Landslide Susceptibility Using Machine Learning Based on Information Value Sampling Method

Evaluation of Landslide Susceptibility Using Machine Learnin...

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2023 China automation Congress, CAC 2023

作者： Liu, Lang Chen, Xin Zhao, Lingran Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Exploration Institute of Geological Engineering of Xiangyang Xiangyang441000 China

ISBN: (纸本)9798350303759

In landslide susceptibility prediction using machine learning (ML) models, the selection of appropriate negative samples significantly impacts the accuracy of the predictions. Many existing studies tend to randomly choose negative samples from the entire research area or specific attribute areas such as low slopes, leading to inaccuracies and biases that decrease the reliability of susceptibility mapping. To address this issue, this paper proposes an ML model based on the coupled information value (IV) method for susceptibility modeling. In this approach, the attribute values of environmental factors are transformed into information values, representing their contribution to landslides, using the information quantity method. Very low and low prone areas are then identified based on these information values, and random points are selected from these areas as negative samples for training and verifying subsequent machine learning models, which include the Random Forest (RF) model and Support Vector Machine (SVM) *** study focuses on the Three Gorges reservoir area in the Zigui-Badong area as an example. The comparative analysis of models trained using traditional sampling methods reveals that the RF model and SVM model, which were sampled using the information value method, achieve accuracies of 95.33% and 91.13%, respectively. In contrast, models trained using traditional sampling methods achieve accuracies of 90.43% and 88.69%, respectively. © 2023 IEEE.

关键词： Landslides

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Machine Learning Classification of Riemannian Tangent Spaces Based on MI-BCI 20

Machine Learning Classification of Riemannian Tangent Spaces...

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20th IEEE International Conference on Mechatronics and automation, ICMA 2023

作者： Zhao, Chunli Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

Motor imagery brain-computer interfaces (BCIs) have been extensively used in stroke rehabilitation, particularly in China where stroke incidence is high. The application of BCI to control prostheses and robotic arms has shown promising results in improving the quality of life for stroke patients. However, a major challenge in this field is improving the accuracy of motor imagery (MI) classification. Several previous studies have reported that stroke patients exhibit atypical patterns of even-trelated desynchronization (ERD) and event-related synchronization (ERS) activation during motor imagery (MI) tasks. It has been suggested that the brain regions affected by stroke may show reduced excitability and relative insensitivity to external interference compared to those of healthy individuals. Therefore, this study proposes that the classification accuracy of stroke patients in the same MI task would be higher compared to that of healthy volunteers. In this study, we employed a feature extraction algorithm that combines the Filter Bank Common Spatial Pattern (FBCSP) and Riemann geometric mean distance methods to compare the classification accuracy of five different classifiers, with the objective of identifying the most suitable classifier for our analysis. The obtained results revealed that the K-Nearest Neighbors (KNN) classifier yielded the highest accuracy, reaching 87.5% for stroke patients and 85% for healthy volunteers, respectively. In addition to accuracy, we also utilized Kappa values as evaluation metrics, which yielded Kappa results of 0.619 and 0.613 for stroke patients and healthy volunteers, respectively. © 2023 IEEE.

关键词： Brain computer interface

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Modeling and control Design for Quadrotor Transporting System With a Cable-Suspended Rigid Payload 20

Modeling and Control Design for Quadrotor Transporting Syste...

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20th IEEE International Conference on Mechatronics and automation, ICMA 2023

作者： Xia, Tianqi Dong, Enzeng Yang, Sen Tong, Jigang Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

In this paper, facing the aerial transportation task with a large volume cargo, the system of quadrotor with a cable-suspended rigid payload is investigated. The dynamic model of proposed system is built with Lagrange d'Alembert principle. The dynamic model is linearized to design the feedback control method. Finally, two groups of numerical simulation with proposed feedback controller are designed to demonstrate the effectiveness of system model and the results of simulation illustrate satisfactory performance. © 2023 IEEE.

关键词： Dynamic models

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Classification of EEG signals based on CNN-Transformer model 20

Classification of EEG signals based on CNN-Transformer model

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20th IEEE International Conference on Mechatronics and automation, ICMA 2023

作者： Liu, Jianwei Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

Brain-computer interfaces (BCI) based on EEG have attracted extensive research and attention worldwide, while motor imagery (MI), mental arithmetic (MA), and P300 event-related potentials are a few of the more commonly used *** Transformer(ViT) is a new Transformer model that has superior global processing power compared to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).In this study, we propose a hybrid CNN-Transformer based model that uses CNN to convolve EEG signals in time and space, followed by ViT for global processing, and finally optimizes the model using 10-run × 10-fold cross-validation and validates it on a publicly available dataset of 29 subjects. Final accuracies of 87.23% and 90.79% were achieved on the MI and MA tasks, respectively. Compared to other literature, the model achieved higher classification accuracies. © 2023 IEEE.

关键词： Brain computer interface

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Improving Transient Response of a Class of Switched Linear systems

Improving Transient Response of a Class of Switched Linear S...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Wang, Miaomiao Sun, Zhendong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781665481090

A switched system is composed by continuous dynamics, logic dynamics, and the interaction between them. For the general class of controllable and reversible discrete-time switched linear control systems, this work presents a computational design scheme that could optimize the transient response in terms of state overshoot and settling time. The scheme is based on a two-step optimization: the first is to design the deadbeat feedback gain matrices by exhaust searching the controllability switching realizations, and the second is to optimize the feedback gain matrices by random perturbations. A numerical example is presented to show the effectiveness of the proposed design scheme. © 2022 IEEE.

关键词： Transient analysis

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Robust-Tube Model Predictive control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers 2

Robust-Tube Model Predictive Control for Piezoelectrically A...

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2nd International Conference on Artificial Intelligence and automation control, AIAC 2024

作者： Chai, Yu Yang, Chen Dong, Huijuan Zhao, Jie Shardt, Yuri A. W. Ju, Bingfeng ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China Technical University of Ilmenau Department of Automation Engineering Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331529147

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%. © 2024 IEEE.

关键词： Predictive control systems

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3D Vision Based Lamination Gap Detection of Carbon Fiber Composite

3D Vision Based Lamination Gap Detection of Carbon Fiber Com...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Xu, Yuanye Yuan, Libiao Liang, Wei Yang, Yutuo Liu, Shuai Zeng, Ziming Zhang, Yinlong Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Shenyang University of Chemical Technology College of Information Engineering Shenyang110142 China Shenzhen Polytechnic School of Automotive and Transportation Engineering Shenzhen518055 China

ISBN: (纸本)9798350327182

The paper presents a machine learning approach to detect laminate gaps and to classify them as open or closed. Compared with the traditional detection methods, which suffer from low precision of point cloud segmentation and poor effect of gap extraction in traditional gap detection, this paper proposes a gap detection algorithm based on 3D Vision. First, an improved undirected graph point cloud segmentation algorithm is used to overcome the under-segmentation of the target region. Second, an adaptive vector for angle discriminant model is proposed to solve the problem of poor extraction effect of the gap edge points. Finally, the B-spline curve-fitting model is used to describe the 3D points on the lamination edges. The proposed algorithm has been evaluated in a visual quality detection platform for carbon fiber composite laminates. The results show that the proposed approach is able to accurately detect and classify laminate gaps in carbon fiber composite structures, providing a reliable tool for quality control in industrial manufacturing processes. © 2023 IEEE.

关键词： Extraction

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 IEEE.

关键词： Convolutional neural networks

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Modeling and Operating Point Analysis for Aquatic Translational Motion of a Cross-Medium Vehicle 42

Modeling and Operating Point Analysis for Aquatic Translatio...

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42nd Chinese control Conference, CCC 2023

作者： Dou, Minghao Liu, Xuchen Huang, Dongyue Wang, Biao Cui, Jinqiang Ren, Qinyuan Dou, Lihua Chen, Jie Chen, Ben M. Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin N.T. Hong Kong College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Peng Cheng Laboratory Guangdong Shenzhen China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou China Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9789887581543

Modeling of cross-medium vehicles with complex shapes still requires a thorough investigation. This paper proposes a multi-method combination modeling approach to tackle such a problem. First-principle model is derived to determinate a model structure. Experiments are then set up to estimate parameters related to its rigid-body model and propulsion system. Computational fluid dynamics (CFD) is performed to calculate and identify coefficients related to surrounding fluid. Base on the model obtained, we systematically investigate the possible steady motion of the cross-medium vehicle and analyze their related performance. Results are instrumental for designing controllers for the vehicle to perform autonoumous missions. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Computational fluid dynamics

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