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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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Mn3O4-g-C3N4 Composite to Activate Peroxymonosulfate for Organic Pollutants Degradation: Electron Transfer and Structure-Dependence

SSRN

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SSRN 2022年

作者： Fan, Jinhong Wang, Qiaoqiao Yan, Wei Chen, Jiabin Zhou, Xuefei Xie, Haijiao State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science & Engineering Tongji University Shanghai200092 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China Hangzhou Yanqu Information Technology Co. Ltd. China

A novel heterogeneous manganese/graphitic carbon nitride (Mn3O4-CN) catalyst for activating peroxymonosulfate (PMS) was successfully assembled by alkali precipitation. The g-C3N4 improved the surface morphology, microporous structure, surface area and particle size distribution of the composite, and an electron rich center with Mn site was created. The Mn3O4-CN/PMS system exhibited high efficacy and stability when the solution pH varied from 3.0 to 9.0, with more than 90% p-acetaminophen (ACT) removal in 30 min under experimental conditions. A possible reaction mechanism was proposed that mainly involved electron transfer from Mn (II) and Mn (III) to PMS along with the generation of ·O2- and 1O2, and the degradation of ACT was attributed to the 1O2. Specifically, it was found that the degradation rate of phenolic compounds varied with their molecular structure, as followed ACT > bisphenol A (BPA) > p-cresol (MP) > p-chlorophenol (CP) > phenol (Ph) > p-nitrophenol (NP). Further, density functional theory (DFT) calculation indicated that the degradation efficiency of phenols was related to their adsorption energy and Bader charge value. All these results improved our understanding of the Manganese-based PMS non-radical dominated process and could predict the degradation of phenols. © 2022, The Authors. All rights reserved.

关键词： Phenols

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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Joint design of the transmit and receive beamforming via non-iterative optimization for MIMO radar

Joint design of the transmit and receive beamforming via non...

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IET International Radar Conference (IRC 2023)

作者： Yujia Li Xiaolin Du Mengjiao Tang Guolong Cui Yao Rong Computer and Control Engineering Yantai University Yantai People's Republic of China Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming People's Republic of China Information and Communication Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China

ISBN: (数字)9781837240982

The joint design of the transmit and receive beamforming for multiple-input multiple-output (MIMO) radar systems is considered in the background of signal dependent interference. An optimization model using signal-to-interference-plus-noise-ratio (SINR) as a performance metric is designed under similarity cone, power, and energy constraints. An algorithm for simultaneous updating of transmit and receive (T-R) beamforming is presented, that bypasses the conventional T-R iteration and initialization, thus avoiding premature termination of the iteration or poor initialization. Simulation experiments verify the effectiveness of the method.

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Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device

Deep Learning Approach for Complex Activity Recognition usin...

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2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics, RI2C 2021

作者： Hnoohom, Narit Jitpattanakul, Anuchit You, Ilsun Mekruksavanich, Sakorn Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand Soonchunhyang University Department of Information Security Engineering Asan31538 Korea Republic of University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand

ISBN: (纸本)9781665403009

The classification of simple and complex sequences of operations is made easier according to the use of heterogeneous sensors from a wearable device. Sensor-based human activity recognition (HAR) is being used in smartphone platforms for elderly healthcare monitoring, fall detection, and inappropriate behavior prevention, such as smoking habit, unhealthy eating, and lack of exercise. Common machine learning and deep learning techniques have recently been presented to tackle the HAR issue, with a focus on everyday activities, particularly general human activities including moving, sitting, and standing. However, there is an intriguing and challenging HAR research subjects involving more complicated psychological activities in various environments, including smoking, eating, and drinking. The use of heterogeneous sensor data to enhance recognition performance over sensor-based deep learning networks is considered in this work. We demonstrate that using a combination of two inertial measurement units outperforms employing either an accelerometer or a gyroscope by utilizing four deep learning classifiers to recognize complex human activity (CHA). Furthermore, we describe the impact of five window sizes (5s - 40s) on a publicly accessible benchmark dataset and how increasing window size effects to the classification performance of CHA deep learning networks. © 2021 IEEE.

关键词： Deep learning

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Sending-or-not-sending twin-field quantum key distribution with multiphoton states

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Physical Review A 2021年第6期104卷 062441-062441页

作者： Jun Teng Zhen-Qiang Yin Guan-Jie Fan-Yuan Feng-Yu Lu Rong Wang Shuang Wang Wei Chen Wei Huang Bing-Jie Xu Guang-Can Guo Zheng-Fu Han CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 People's Republic of China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 People's Republic of China Science and Technology on Communication Security Laboratory Institute of Southwestern Communication Chengdu Sichuan 610041 China State Key Laboratory of Cryptography P.O. Box 5159 Beijing 100878 People's Republic of China

The twin-field quantum key distribution (TF-QKD) protocol has led to extensive investigation for its capacity of overcoming the rate-distance limit. One variant for TF-QKD named sending-or-not-sending (SNS) TF-QKD has also been proposed. The SNS-TF-QKD encodes the classical keys 0 and 1 in sending or not sending the phase-randomized coherent states, which helps it to tolerate large misalignment errors due to optical phase fluctuations. Nevertheless, the original SNS-TF-QKD scheme utilizes only single-photon interference in generating secret keys, which seems quite different from other TF-QKD protocols. In this paper, we prove that multiphoton interference in the SNS TF-QKD protocol can also contribute to key generation. Accordingly, an improved SNS-TF-QKD scheme with higher key rate is proposed, where events of not only single-photon but also two-photon emissions are considered as effective events.

关键词： Quantum cryptography

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Identifying driver genes in cancer based on Pareto optimality consensus

Identifying driver genes in cancer based on Pareto optimalit...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zheng Deng Jingli Wu Xiaorong Chen Gaoshi Li College of Computer Science Information Technology Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision Intelligent Control Wuzhou University Wuzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases.

关键词： Weight measurement Databases Medical treatment Genomics Pareto optimization Biomarkers Nonhomogeneous media

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Autonomous Cyber Defense Agents for NATO: Threat Analysis, Design, and Experimentation

Autonomous Cyber Defense Agents for NATO: Threat Analysis, D...

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MILCOM, Military communications Conference

作者： Alexander Velazquez Roberto Rigolin F. Lopes Adrien Bécue Johannes F. Loevenich Paulo H. L. Rettore Konrad Wrona Information Technology Division U.S. Naval Research Laboratory Washington DC USA Secure Communications & Information (SIX) Thales Deutschland Ditzingen Germany THALES SIX GTS Gennevilliers France Department of Mathematics/Computer Science University of Osnabrück Osnabrück Germany Department of Communication Systems Fraunhofer FKIE Bonn Germany Military University of Technology Warsaw Poland NATO Cyber Security Centre The Hague The Netherlands

This paper discusses the design and implementation of Autonomous Cyber Defense (ACD) agents for Protected Core Networking (PCN). Our solution includes two types of specialized, complementary agents placed in different parts of the network. One type of agent, ACD-Core, is deployed within the protected core segment of a particular nation and can monitor and act in the physical and IP layers. The other, ACD-CC, is deployed within a colored cloud and can monitor and act in the transport and application layers. We analyze the threat landscape and identify possible uses and misuses of these agents. Our work is part of an ongoing collaboration between two NATO research task groups, IST-162 and IST-196. The goal of this collaboration is to detail the design and roadmap for implementing ACD agents for PCN and to create a virtual lab for related experimentation and validation. Our vision is that ACD will contribute to improving the cybersecurity of military networks, protecting them against evolving cyber threats, and ensuring connectivity at the tactical edge.

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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Multi-Functional Beamforming Design for Integrated Sensing, communication, and Computation

arXiv

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arXiv 2024年

作者： Zhao, Yapeng Wu, Qingqing Chen, Wen Zeng, Yong Liu, Ruiqi Mei, Weidong Hou, Fen Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China The Department of Electronic Engineering Shanghai Jiao Tong University 200240 China The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation Shenzhen518057 China The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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