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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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Optimization Strategy of Bio-Inspired Metaheuristic Algorithms Tuned PID controller for PMBDC Actuated Robotic Manipulator 3

Optimization Strategy of Bio-Inspired Metaheuristic Algorith...

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3rd International Conference on Computing and Network Communications, CoCoNet 2019

作者： Valluru, Sudarshan K. Singh, Madhusudan Center for Control of Dynamical Systems and Computation Department of Electrical Engineering Delhi Technological University Delhi110042 India

The present article makes an attempt to evaluate the computational performance of genetic meta heuristic optimized control algorithms. Here, the multi-objective bio inspired algorithm (MOGA) and adaptive particle swarm optimization (APSO) algorithm are used to tune linear PID (L-PID) and nonlinear PID(NL-PID) controllers to implement performance and execution control of permanent magnet brushed DC (PMBDC) motor actuated robotic manipulator. The MOGA, APSO optimised nonlinear and linear PID controllers have been validated for their response efficacy and compared in respect of the steady-state error, overshoot and settling time of PMBDC driven robotic Manipulator. The efficacy of metaheuristics such as APSO and MOGA tuned NL-PID controller are better as compared to the L- PID controlled objects. Experimental results show that the NL-PID controller mends the controlled objects' performace with a reduction in both the overshoot as well as the settling time, if tuned either with MOGA or APSO for L-PID controllers while NL-PID controller tuned with APSO gives satisfactory dynamic response of the system. © 2020 The Authors. Published by Elsevier B.V.

关键词： Permanent magnets

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Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

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IEEE Journal of Biomedical and Health Informatics 2023年第8期27卷 3830-3843页

作者： He, Congying Chen, Yu-Yi Phang, Chun-Ren Stevenson, Cory Chen, I-Ping Jung, Tzyy-Ping Ko, Li-Wei Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Applied Arts National Yang Ming Chiao Tung University Hsinchu30010 Taiwan International Ph.D. Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San DiegoCA90093 United States National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems. © 2023 IEEE.

关键词： Electroencephalography

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ONLINE FEEDBACK OPTIMIZATION AND SINGULAR PERTURBATION VIA CONTRACTION THEORY∗

arXiv

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arXiv 2023年

作者： Cothren, Liliaokeawawa Bullo, Francesco Dall’Anese, Emiliano Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93101 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States

In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems, including those commonly encountered in Online Feedback Optimization (OFO). Our framework endows these systems with several robustness properties, enabling a more comprehensive characterization of their behaviors. The primary assumptions are the contractivity of the fast subsystem and the reduced model, along with an explicit upper bound on the time-scale parameter. For two-time scale systems subject to disturbances, we show that the distance between solutions of the nominal system and solutions of its reduced model is uniformly upper bounded by a function of contraction rates, Lipschitz constants, the time-scale parameter, and the variability of the disturbances over time. Applying these general results to the OFO context, we establish new individual tracking error bounds, showing that solutions converge to their time-varying optimizer, provided the plant and steady-state feedback controller exhibit contractivity and the controller gain is suitably bounded. Finally, we explore two special cases: for autonomous nonlinear systems, we derive sharper bounds than those in the general results, and for linear time-invariant systems, we present novel bounds based on induced matrix norms and induced matrix log norms. Copyright © 2023, The Authors. All rights reserved.

关键词： State feedback

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Experimental Investigation of Fully Informed Particle Swarm Optimization tuned Multi Loop L-PID and NL-PID controllers for Gantry Crane System 3

Experimental Investigation of Fully Informed Particle Swarm ...

引用

3rd International Conference on Computing and Network Communications, CoCoNet 2019

作者： Valluru, Sudarshan K. Kaur, Manpreet Kartikeya, Kumar Goel, Arnav Dobhal, Daksh Incubation Center for Control Dynamical Systems and Computation Department of Electrical Engineering Delhi Technological University Delhi110042 India

This paper presents a novel stabilizing nonlinear PID controller with real time experimental validation on a gantry crane system. Our adaptive control technique describes the underactuated system as a nonlinear mapping of control signal with the hyperbolic tangent function. The proposed function is used to impose a constraint on the proportional, integral and derivative errors such that the system results an effective and robust performance. The main trait of the controller is to bound the high control error to result in a lesser correction value. An enhanced hybrid method based on the bioinspired meta-heuristic algorithm Particle Swarm Optimisation is utilised to tune the gain coefficients Kpδ, KIδ and KDδ. The Linear controller and the proposed controllers were compared based on the stabilizing action on the real time system. The real time results depict that the nonlinear PID performs better than the conventional PID. © 2020 The Authors. Published by Elsevier B.V.

关键词： controllers

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State Estimation for Asynchronously Switched Sampled-Data Systems

State Estimation for Asynchronously Switched Sampled-Data Sy...

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IEEE Conference on Decision and control

作者： Sharad C. Shankar Guosong Yang João P. Hespanha Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA Department of Electrical and Computer Engineering Rutgers University–New Brunswick Piscataway NJ USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Asynchronously switched sampled-data systems can help model power systems and vehicles that evolve in continuous-time with switching behavior and discrete time measurements. We address the problem of jointly estimating a switching signal, with uncertainty in the exact switching times, as well as the continuous states of the system. We prove stability of the standard Kalman Filter under uncertainty in the switching times, with statistical bounds relating to the sampling period. We then propose a method for estimation of switching times as well as a method for efficient joint estimation of the state and switching signal inspired by the interacting multiple-model extended-Viterbi algorithm. We validate our algorithms in simulation for a power converter and a maneuvering vehicle.

关键词： Uncertainty Heuristic algorithms Power system dynamics Switches Power system stability Filtering algorithms Sampled data systems

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Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning

arXiv

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arXiv 2022年

作者： Centorrino, Veronica Bullo, Francesco Russo, Giovanni Scuola Superiore Meridionale Naples Italy The Department of Mechanical Engineering The Center for Control Dynamical Systems and Computation University of California Santa Barbara United States The Department of Information and Electric Engineering and Applied Mathematics University of Salerno Italy

This paper is concerned with the modeling and analysis of two of the most commonly used recurrent neural network models (i.e., Hopfield neural network and firing-rate neural network) with dynamic recurrent connections undergoing Hebbian learning rules. To capture the synaptic sparsity of neural circuits we propose a low dimensional formulation. We then characterize certain key dynamical properties. First, we give biologically-inspired forward invariance results. Then, we give sufficient conditions for the non-Euclidean contractivity of the models. Our contraction analysis leads to stability and robustness of time-varying trajectories - for networks with both excitatory and inhibitory synapses governed by both Hebbian and anti-Hebbian rules. For each model, we propose a contractivity test based upon biologically meaningful quantities, e.g., neural and synaptic decay rate, maximum in-degree, and the maximum synaptic strength. Then, we show that the models satisfy Dale's Principle. Finally, we illustrate the effectiveness of our results via a numerical example. Copyright © 2022, The Authors. All rights reserved.

关键词： Hopfield neural networks

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Euclidean Contractivity of Neural Networks with Symmetric Weights

arXiv

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arXiv 2023年

作者： Centorrino, Veronica Gokhale, Anand Davydov, Alexander Russo, Giovanni Bullo, Francesco Scuola Superiore Meridionale University of Naples Federico II Italy Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93106 United States Department of Information and Electric Engineering and Applied Mathematics University of Salerno Italy

This paper investigates stability conditions of continuous-time Hopfield and firing-rate neural networks by leveraging contraction theory. First, we present a number of useful general algebraic results on matrix polytopes and products of symmetric matrices. Then, we give sufficient conditions for strong and weak Euclidean contractivity, i.e., contractivity with respect to the 2 norm, of both models with symmetric weights and (possibly) non-smooth activation functions. Our contraction analysis leads to contraction rates which are log-optimal in almost all symmetric synaptic matrices. Finally, we use our results to propose a firing-rate neural network model to solve a quadratic optimization problem with box constraints. © 2023, CC BY.

关键词： Matrix algebra

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PDMATLAB2D: A Peridynamics MATLAB Two-dimensional Code

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Journal of Peridynamics and Nonlocal Modeling 2024年第1期6卷 149-205页

作者： Seleson, Pablo Pasetto, Marco John, Yohan Trageser, Jeremy Reeve, Samuel Temple Computer Science and Mathematics Division Oak Ridge National Laboratory P.O. Box 2008 MS-6013 Oak Ridge 37831-6013 TN United States Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Drive La Jolla 92093 CA United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology 801 Ferst Dr Atlanta 30332 GA United States Center for Control Dynamical Systems and Computation University of California at Santa Barbara Harold Frank Hall Room 5119 Santa Barbara 93106 CA United States Engineering Sciences Center Sandia National Laboratories P.O. Box 5800 MS-1322 Albuquerque 87185-1322 NM United States Computational Sciences and Engineering Division Oak Ridge National Laboratory P.O. Box 2008 MS-6085 Oak Ridge 37831-6085 TN United States

PDMATLAB2D is a meshfree peridynamics implementation in MATLAB suitable for simulation of two-dimensional fracture problems. The purpose of this code is twofold. First, it provides an entry-level peridynamics computational tool for educational and training purposes. Second, it serves as an accessible and easily modifiable computational tool for peridynamics researchers who would like to adapt the code for a multitude of peridynamics simulation scenarios. The current version of the code implements a bond-based brittle elastic peridynamic model and a critical stretch criterion for bond breaking. However, the code is designed to be extendable for other peridynamic models and computational features. In this paper, we provide an overview of the code structure and functions with illustrative examples. Due to the integrated computation and postprocessing MATLAB capabilities, PDMATLAB2D can serve as an effective testbed for testing new constitutive models and advanced numerical features for peridynamics computations. © UT-Battelle, LLC, under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： Fracture Meshfree Peridynamics Two-dimensional

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Contraction Analysis of Hopfield Neural Networks with Hebbian Learning

Contraction Analysis of Hopfield Neural Networks with Hebbia...

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IEEE Conference on Decision and control

作者： Veronica Centorrino Francesco Bullo Giovanni Russo Scuola Superiore Meridionale University of Naples Federico II Italy Department of Mechanical Engineering Center for Control Dynamical Systems and Computation University of California Santa Barbara USA Department of Information and Electric Engineering and Applied Mathematics University of Salerno Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Motivated by advances in neuroscience and machine learning, this paper is concerned with the modeling and analysis of Hopfield neural networks with dynamic recurrent connections undergoing Hebbian learning. To capture the synaptic sparsity of neural circuits, we propose a low dimensional formulation for the model and then characterize its key dynamical properties. First, we give a biologically-inspired forward invariance result. Then, we give sufficient conditions for the non-Euclidean contractivity of the model. Our contraction analysis leads to stability and robustness of time-varying trajectories – for networks with both excitatory and inhibitory synapses governed by both Hebbian and anti-Hebbian rules. Our proposed contractivity test is based upon biologically meaningful quantities, e.g., neural and synaptic decay rate, maximum out-degree, and the maximum synaptic strength. Finally, we show that the model satisfies Dale’s principle. The effectiveness of our results is illustrated via a numerical example.

关键词： Sufficient conditions Biological system modeling Hopfield neural networks Stability analysis Robustness Trajectory Integrated circuit modeling

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