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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Linear Encryption Techniques for Counteracting Information-based Stealthy Attacks

Linear Encryption Techniques for Counteracting Information-b...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Jun Shang Hanwen Zhang Weixiong Rao Yiguang Hong Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Sci-ence and Technology Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

关键词： Technological innovation Automation Wireless networks Encryption Numerical models Kalman filters State estimation Robots Intelligent sensors

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Game Theory for Autonomy: From Min-Max Optimization to Equilibrium and Bounded Rationality Learning

Game Theory for Autonomy: From Min-Max Optimization to Equil...

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American control Conference (ACC)

作者： Kyriakos G. Vamvoudakis Filippos Fotiadis João P. Hespanha Raphael Chinchilla Guosong Yang Mushuang Liu Jeff S. Shamma Lacra Pavel Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Center for Control Dynamical Systems and Computation University of California Santa Barbara CA Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA Department of Mechanical and Aerospace Engineering University of Missouri Columbia MO USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign IL USA Department of Electrical and Computer Engineering University of Toronto Canada

Finding Nash equilibria in non-cooperative games can be, in general, an exceptionally challenging task. This is owed to various factors, including but not limited to the cost functions of the game being nonconvex/nonconcave, the players of the game having limited information about one another, or even due to issues of computational complexity. The present tutorial draws motivation from this harsh reality and provides methods to approximate Nash or min-max equilibria in non-ideal settings using both optimization- and learning-based techniques. The tutorial acknowledges, however, that such techniques may not always converge, but instead lead to oscillations or even chaos. In that respect, tools from passivity and dissipativity theory are provided, which can offer explanations about these divergent behaviors. Finally, the tutorial highlights that, more frequently than often thought, the search for equilibrium policies is simply vain; instead, bounded rationality and non-equilibrium policies can be more realistic to employ owing to some players’ learning imperfectly or being relatively naive – "bounded rational." The efficacy of such plays is demonstrated in the context of autonomous driving systems, where it is explicitly shown that they can guarantee vehicle safety.

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LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Sun, Songkai She, Qingshan Ma, Yuliang Li, Rihui Zhang, Yingchun The School of Automation Hangzhou Dianzi University Zhejiang Hangzhou310018 China International Joint Research Laboratory for Autonomous Robotic Systems Zhejiang Hangzhou China The Center for Cognitive and Brain Sciences The Department of Electrical and Computer Engineering University of Macau China The Department of Biomedical Engineering University of Miami Coral GablesFL United States

In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to its high complexity. To address this challenge, we proposed a new lightweight U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It utilized an asymmetrical structural design and incorporated both local and global modules to enhance its capacity for local and global modeling. Additionally, a multi-layer cascade fusion decoding network was designed to further bolster the network's information fusion capabilities. Validation results achieved on multi-organ datasets in CT format, cardiac segmentation datasets in MRI format, and dermatology datasets in image format demonstrated that the proposed model outperformed other state-of-the-art methods in handling local-global information, achieving an improvement of 1.54% in Dice coefficient and 2.6 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a network that combines Convolutional Neural Network and Transformer architectures, it achieves competitive segmentation performance with only 6.93 million parameters and 6.6 gigabytes of floating point operations, without the need of pre-training. In summary, the proposed method demonstrated enhanced performance while retaining a simpler model design compared to other Transformer-based segmentation networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Improving Magnetic Concrete Permeability by Electromagnetic Pulse-Enhanced Anisotropy

Improving Magnetic Concrete Permeability by Electromagnetic ...

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Annual Conference of Industrial Electronics Society

作者： Xiuhu Sun Jiale Zhou Shen-En Chen Tiefu Zhao Electrical and Computer Engineering Department University of North Carolina at Charlotte Charlotte NC USA Civil & Environmental Engineering Department University of North Carolina at Charlotte Charlotte NC USA North Carolina Battery Complexity Autonomous Vehicle and Electrification (BATT CAVE) Research Center University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Magnetic concrete (MC) containing cement and magnetic particles is gaining increasing attention. Current research on improving MC permeability mainly focuses on adopting high-performance materials and adjusting magnetic particle sizes and proportions. These methods are effective but often result in increased costs. Enhancing magnetic materials’ anisotropy can improve permeability without adding additional cost. However, existing research has been relatively insufficient in exploring this issue. This paper proposes to use pulsed electromagnetic fields to improve the anisotropy of MC, thereby increasing the permeability. The results indicate that pulses can enhance the permeability (µ r ) of magnetic cores made of particle material by 8.12 % to 24.58 %, depending on the particle size, and increase the permeability of magnetic cores made of MC by 2.79 % without increasing costs.

关键词： Industrial electronics Costs Anisotropic magnetoresistance Magnetic particles Magnetic cores Cement industry Permeability Electromagnetic fields Perpendicular magnetic anisotropy Electromagnetics

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Optimizing Magnetic Block Structure for Enhanced Coupling Coefficients in Wireless Power Transfer: A Finite Element Analysis Approach

Optimizing Magnetic Block Structure for Enhanced Coupling Co...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Xiuhu Sun Karl Lin Jiale Zhou Shen-En Chen Nicole Braxtan Tiefu Zhao Electrical and Computer Engineering Department University of North Carolina at Charlotte Charlotte NC USA Civil & Environmental Engineering Department University of North Carolina at Charlotte Charlotte NC USA North Carolina Battery Complexity Autonomous Vehicle and Electrification (BATT CAVE) Research Center University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

Magnetic components play a pivotal role in enhancing the coupling coefficient within wireless power transfer (WPT) systems. Conventional manufacturing of these components involves high-pressure compaction and high-temperature sintering, resulting in rigid, fixed-shape products that limit the impact of shape variations on WPT system coupling coefficients. This paper employs finite element analysis (FEA) tools to investigate the influence of different magnetic component shapes on coupling coefficients in planar WPT systems. First, through analyzing the magnetic circuit, a method of setting grooves on the magnetic block (MB) is proposed. Second, a multi-group and narrow-range (MGNR) simulation method is proposed to identify the critical parameters of the grooves for an optimal coupling coefficient. Then, reference MBs without grooves and optimized MBs with grooves are manufactured with cement and ferrite particles. Finally, a prototype is constructed to evaluate the performance of the two types of MBs. The test results demonstrate a 7.51% enhancement in the coupling coefficient for the optimized MBs compared to the reference MBs, accompanied by a reduction of 28.24% in material usage. These findings underscore the effectiveness of this approach in improving the coupling coefficients of WPT systems. Furthermore, shortcomings of this approach, including minor adverse effects on efficiency, are also identified.

关键词： Couplings Shape Transportation Sintering Prototypes Wireless power transfer Magnetic analysis

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Static and Dynamic Analysis of Wireless Power Transfer for Battery-Electric Locomotives

Static and Dynamic Analysis of Wireless Power Transfer for B...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Xiuhu Sun Xiwen Xu Karl Lin Shen-En Chen Tiefu Zhao Electrical and Computer Engineering Department University of North Carolina at Charlotte Charlotte NC USA Civil & Environmental Engineering Department University of North Carolina at Charlotte Charlotte NC USA North Carolina Battery Complexity Autonomous Vehicle and Electrification (BATT CAVE) Research Center University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

Wireless power transfer (WPT), particularly inductive power transfer (IPT), has been extensively researched in the electric vehicle (EV) domain due to its ability to enable automatic charging without manual intervention and its potential for charging while in motion. However, its implementation in battery-electric locomotives has not garnered sufficient attention, especially concerning the dynamic aspect. This study proposes a multi-transmitter single-receiver (MTSR) WPT system tailored for railway applications, employing a parallel synchronous multi-LCL-s compensation topology and a W-I-shaped coupler. The dynamic charging process of this system is analyzed, and methods for calculating the coupling coefficient and output during dynamic charging are derived. A prototype with one receiver and three transmitters is constructed. Static testing utilizing a single-transmitter and single-receiver (STSR) setup reveals that the system maintains a DC-DC efficiency of over 92% with an input DC voltage ranging from 200 to 500 V and an output load from 15.5 to 34 Ω. Notably, at an input power of 3.1 kW, voltage of 705V, and load resistance of 21 Ω, the system achieves a maximum efficiency of 94.73%. Dynamic testing results demonstrate that with an input power of 3 kW, voltage of 500V, and load resistance of 15 Ω, the highest dynamic efficiency reaches around 89% and remains stable at this level. Furthermore, the testing results validate the consistency between the system’s output characteristics and the calculated results, thereby verifying the rationale of the calculation methods.

关键词： Resistance Transmitters Dynamics Transportation Wireless power transfer Voltage Receivers

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Traffic Signal control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-based Sensors

Traffic Signal Control for Large-Scale Urban Traffic Network...

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International Conference on control and Automation (ICCA)

作者： Jiho Park Tong Liu Chieh Wang Hong Wang Qichao Wang Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA National Renewable Energy Laboratory Computational Science Center Golden CO USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.

关键词： Energy consumption Urban areas Decentralized control Data processing Real-time systems Sensors Delays

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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Enhanced pH sensing with Ce-doped YTixOy sensing membrane in high-performance electrolyte–insulator–semiconductor devices

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Materials Chemistry and Physics 2024年 311卷

作者： Singh, Kanishk Tai, Li-Chia Her, Jim-Long Pan, Tung-Ming Department of Electronics Engineering Chang Gung University Taoyuan33302 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electrical and Control Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Division of Natural Science Center for General Education Chang Gung University Taoyuan33302 Taiwan Division of Urology Chang Gung Memorial Hospital Taoyuan33305 Taiwan

Electrolyte-insulator-semiconductor (EIS)-based sensors are a novel class of electronic chips designed for biochemical sensing that offer a direct electronic readout, making them part of a new generation of sensing technologies. To enhance the pH sensitivity, in this research we fabricated the CeYTixOy-based EIS sensors with different Ce concentrations (1 %, 5 %, and 10%) by the solution-processing method. The crystal orientation, surface topography, and chemical composition of the sensing film were examined by X-ray diffraction, atomic force microscopy, and X-ray photoelectron spectroscopy, respectively. The CeYTixOy-based EIS sensor treated with an optimal 5% Ce concentration displays a Super-Nernstian pH response of 66.59 mV/pH with an excellent linearity of 0.99. In the context of reliability, the device exhibited a smaller hysteresis voltage of 7 mV and a lower drift rate of 0.75 mV/h. This result may be attributed to the optimal Ce content in the CeYTixOy leads to the formation of redox couple Ce4+/Ce3+ resulting in less than one electron transferred per proton in the redox reaction. © 2023 Elsevier B.V.

关键词： Cerium compounds

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