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A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges

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Biomedical engineering Letters 2025年 1-32页

作者： Lee, Hyung-Tak Shim, Miseon Liu, Xianghong Cheon, Hye-Ran Kim, Sang-Gyu Han, Chang-Hee Hwang, Han-Jeong Department of Electronics and Information Engineering Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Department of Artificial Intelligence Tech University of Korea Siheung Korea Republic of Department of Computer Information Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea Republic of

Human–computer interaction (HCI) focuses on designing efficient and intuitive interactions between humans and computer systems. Recent advancements have utilized multimodal approaches, such as electroencephalography (EEG)-based systems combined with other biosignals, along with deep learning to enhance performance and reliability. However, no systematic review has consolidated findings on EEG-based multimodal HCI systems. This review examined 124 studies published from 2016 to 2024, retrieved from the Web of Science database, focusing on hybrid EEG-based multimodal HCI systems employing deep learning. The keywords used for evaluation were as follows: ‘Deep Learning’ AND ‘EEG’ AND (‘fNIRS’ OR ‘NIRS’ OR ‘MEG’ OR ‘fMRI’ OR ‘EOG’ OR ‘EMG’ OR ‘ECG’ OR ‘PPG’ OR ‘GSR’). Main topics explored are: (1) types of biosignals used with EEG, (2) neural network architectures, (3) fusion strategies, (4) system performance, and (5) target applications. Frequently paired signals, such as EOG, EMG, and fNIRS, effectively complement EEG by addressing its limitations. Convolutional neural networks are extensively employed for spatio-temporal-spectral feature extraction, with early and intermediate fusion strategies being the most commonly used. Applications, such as sleep stage classification, emotion recognition, and mental state decoding, have shown notable performance improvements. Despite these advancements, challenges remain, including the lack of real-time online systems, difficulties in signal synchronization, limited data availability, and insufficient explainable AI (XAI) methods to interpret signal interactions. Emerging solutions, such as portable systems, lightweight deep learning models, and data augmentation techniques, offer promising pathways to address these issues. This review highlights the potential of EEG-based multimodal HCI systems and emphasizes the need for advancements in real-time interaction, fusion algorithms, and XAI to enhance their adaptability, interpreta

关键词： Convolutional neural networks

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Mussel-inspired sulfated hyaluronan cryogel patch with antioxidant, anti-inflammatory, and drug-loading properties for multifunctional wound adhesives

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Bioactive Materials 2024年第10期40卷 582-596页

作者： Wonmoon Song Young Hwan Choi Young Gi Moon Changyub Lee M.Nivedhitha Sundaram Nathaniel S.Hwang School of Chemical and Biological Engineering Institute for Chemical ProcessesSeoul National UniversitySeoul08826Republic of Korea Interdisciplinary Program in Bioengineering Seoul National UniversitySeoul08826Republic of Korea Bio-MAX/N-Bio Institute Institute of BioEngineeringSeoul National UniversitySeoul08826Republic of Korea Division of Pediatric Cardiac Surgery Department of SurgeryJohns Hopkins School of MedicineBaltimoreMDUSA Institute of Engineering Research Seoul National UniversitySeoul08826Republic of Korea

Wounds, characterized by the disruption of the continuity of body tissues resulting from external trauma,manifest in diverse types and locations. Although numerous wound dressings are available for various woundscenarios, it remains challenging to find an integrative wound dressing capable of addressing diverse woundsituations. We focused on utilizing sulfated hyaluronan (sHA), known for its anti-inflammatory properties andcapacity to load cationic drugs. By conjugating catechol groups to sHA (sHA-CA), we achieved several advantagesin wound healing: 1) Fabrication of patches through crosslinking with catechol-modified high-molecularweighthyaluronan (HA(HMW)-CA), 2) Adhesiveness that enabled stable localization, 3) Radical scavenging thatcould synergize with the immunomodulation of sHA. The sHA-CA patches demonstrated therapeutic efficacy inthree distinct murine wound models: diabetic wound, hepatic hemorrhage, and post-surgical adhesion. Collectively,these findings underscore the potential of the sHA-CA patch as a promising candidate for the nextgenerationwound dressing.

关键词： Adhesive patch Anti-inflammation Sulfated hyaluronic acid Post-surgical adhesion Diabetic wound Hemostasis

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Distributed Event-triggered Fixed-time Optimal Dispatch Control for Cyber-Physical Islanded DC Microgrids Incorporating Time Delays

Distributed Event-triggered Fixed-time Optimal Dispatch Cont...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Zaery, Mohamed Amrr, Syed Muhammad Hussain, S.M. Suhail Abido, Mohammad A. Control & Instrumentation Engineering Department Saudi Arabia K.A.CARE Energy Research & Innovation Center Saudi Arabia King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Sustainable Energy Systems Saudi Arabia Electrical Engineering Department Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Saudi Arabia

ISBN: (纸本)9798350395075

This study proposes a distributed Event-triggered fixed-time (ET -FxT) cooperative control strategy for optimizing generation costs in DC Microgrids (MGs), which is subjected to communication resource constraints and input time delays. Initially, Artstein's reduction method was applied to transform the time-delay system into a model without delays, aiming to improve system stability. Following this, an ET -FxT distributed algorithm is introduced for cost minimization, designed to balance the incremental costs (I Cs) of all distributed generators (DGs) within an accelerated settling time, while respecting the bandwidth constraints of communication network. Additionally, a distributed control approach is presented for achieving FxT voltage stability, ensuring a balance between generations and consumptions in the MG. The validity of this control method is demonstrated through numerous comprehensive case studies. © 2024 IEEE.

关键词： Microgrids

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Humanoid robot teleoperation system using a fast vision-based pose estimation and refinement method

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IEIE Transactions on Smart Processing and Computing 2021年第1期10卷 24-30页

作者： Sa, Jae-Min Choi, Kang-Sun Interdisciplinary Program on Creative Engineering KOREATECH Cheonan-si31253 Korea Republic of Department of Electrical Electronics and Communication Engineering KOREATECH Cheonan-si31253 Korea Republic of

Humanoid robot teleoperation is a complex task that requires sophisticated hardware and software interfaces. Three-dimensional human pose estimation can be effectively applied to the teleoperation of humanoid robots thanks to its intuitive and easy usability. However, when the human pose is wrongly inferred, it can cause critical and dangerous situations for robot operation. In this paper, a reliable humanoid robot teleoperation system is proposed, which involves the operation of a humanoid robot in an isolated location. The status of the humanoid robot is monitored and communicated to the operator in real time. This improves the telepresence of scenes compared to other methods. On the operator's side, data are retrieved from the humanoid robots and manipulated to control the robot by estimating the operator's pose visually. To reduce the risk of inaccurate pose estimation, pose candidates obtained from different points of view are fused while removing irrelevant poses. Then, a simple yet effective cascade filter that generates smooth motion changes is used for stable and flexible robot manipulation. Experiments confirmed that the proposed system can functions efficiently in real time and generates more accurate motion compared to conventional systems. © 2021 The Institute of Electronics and Information Engineers

关键词： Anthropomorphic robots

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A Common-Ground Seven-Level Boosting Inverter (CG7LI) for Solar-PV Application

A Common-Ground Seven-Level Boosting Inverter (CG7LI) for So...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Ali, Mohammad Zaid, Mohammad Sarwar, Adil Iqbal, Atif Hossain, Md. Ismail Khalid, Muhammad K.A.CARE-ERIC Dhahran31261 Saudi Arabia Aligarh Muslim University Department of Electrical Engineering Aligarh202001 India Qatar University Department of Electrical Engineering Doha2713 Qatar Kfupm Interdisciplinary Research Center for Renewable Energy and Power Systems Dhahran31261 Saudi Arabia Kfupm Electrical Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9781665471640

In this work, a new seven-level inverter architecture for common-ground single-phase photovoltaic (PV) systems with boosting capability is proposed. The size of the passive elements and the number of power components have been reduced in the suggested inverter. For it to function properly, fourteen switches and three capacitors are needed. The number of levels, switches, capacitors, and diodes in the inverter circuit, as well as boosting (step-up capability), common grounding capability (CG), and leakage current value as useful parameters for evaluating inverter performance, are compared with recent multilevel inverter topologies in a comparative study. The proposed seven-level inverter was created in the PLECS environment for its validation. © 2023 IEEE.

关键词： boosting capability clean energy DC-AC converter multilevel operation nearest level control

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Photoresponse of Thermally Evaporated Copper Oxide-Based Solar Cells 1

Photoresponse of Thermally Evaporated Copper Oxide-Based Sol...

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1st Middle East and North Africa Solar Conference, MENA-SC 2023

作者： Alami, Abdul Hai Ayoub, Mohamad Alashkar, Adnan Alasad, Shamma Mdallal, Ayman Khuri, Siren Aljaghoub, Haya University of Sharjah Sustainable Energy & Power Systems Research Centre Sustainable and Renewable Energy Engineering Department Sharjah United Arab Emirates American University of Sharjah Materials Science and Engineering PhD Program Sharjah United Arab Emirates American University of Sharjah Mechancial Engineering Department Sharjah United Arab Emirates University of Sharjah Sustainable Energy & Power Systems Research Centre Sharjah United Arab Emirates University of Sharjah Industrial Engineering and Engineering Management Department Sharjah United Arab Emirates

ISBN: (纸本)9781665491464

This study reports on the fabrication and characterization of a copper oxide (CuO)-based photoactive cell. Copper was thermally evaporated onto two substrates and post-treated using chemical and thermal treatments to obtain different semiconducting types of CuO. Absorption measurements were conducted to obtain the bandgaps of CuO, which were found to be 1.51 eV and 1.21 eV, for the chemically and thermally treated CuO, respectively. Finally, a chronoamperometry test was conducted to confirm the photoresponse of the cell, which showed a doubling of output current values under light conditions compared to dark conditions. © 2023 IEEE.

关键词： Copper oxides

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A Data-Driven Approach Towards Software Regression Testing Quality Optimization 25

A Data-Driven Approach Towards Software Regression Testing Q...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moubayed, Abdallah Alhindawi, Nouh Alsakran, Jamal Injadat, MohammadNoor Kanan, Mohammad King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Arizona State University School of Computing and Augmented Intelligence GilbertAZ United States Jadara University Department of Software Engineering Irbid Jordan Fairleigh Dickinson University Canada Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees. © 2024 IEEE.

关键词： Software testing

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Deep Learning-Based Positioning Error Fault Diagnosis of Wire Bonding Equipment and an Empirical Study for IC Packaging

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IEEE Transactions on Semiconductor Manufacturing 2023年第4期36卷 619-628页

作者： Kao, Sheng-Xiang Chien, Chen-Fu International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Intelligent Manufacturing and Circular Economy Research Center National Tsing Hua University Artificial Intelligence for Intelligent Manufacturing Systems Research Center National Science and Technology Council Department of Industrial Engineering and Engineering Management Hsinchu300044 Taiwan

Little research has been done for artificial intelligence applications of semiconductor backend. This study aims to develop a deep learning based fault diagnosis framework as prognostics and health management (PHM) solutions with a comprehensive analytics process. A linear encoder sensor is employed to measure position, while data preparation is conducted to convert position information into a signal. Then, signal processing is used to extract the features, in which high pass filter is applied to normalize the signal and emphasize the features. High-dimensional features including time domain statistical features, time-frequency domain features obtained by continuous wavelet transform (CWT), and frequency domain features converted by fast Fourier transform (FFT) are extracted to prepare the dataset. An ensemble neural network model that integrates deep neural network (DNN) and convolutional neural network (CNN) is employed to recognize the pattern and diagnose the positioning errors. The accuracy and false alarm rate are considered to support maintenance decisions. An empirical study was conducted in a world leading IC assembly and testing company for validation. The results have shown that the proposed approach can effectively detect inappropriate installation of the bond head in wire bonding equipment for predictive maintenance and cost reduction. © 1988-2012 IEEE.

关键词： Failure analysis

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Localization Algorithms for Drones in a Wireless Communication Environment with Presence of Data Missing

Localization Algorithms for Drones in a Wireless Communicati...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Sin Kim Sung Shin Nak Yong Ko Sung Hyun You Department of Electronic Engineering Chosun University Gwangju Korea Department of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System

Drones are widely used in industries because drones perform tasks in various environments. Drones are highly utilized because drones are not affected by ground conditions. Estimating the position of the drone is important for the successful completion of the missions that are assigned to the drone. In wireless communication environments, data missing often occurs between the anchors serve as the transmitter and the drone-attached tag serves as the receiver. In this paper, we propose a localization algorithm that can accurately estimate the location of the drone in the case of data missing. The proposed algorithm can estimate the position of the drone using the residuals of the predicted measurements in the case of data missing. The proposed algorithm aimed to estimate the position of the drone in the case of data missing using distance information between the anchors and the drone-attached tag and verified the performance of the proposed algorithm through simulation.

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Introducing spectral attention for long-range dependency in time series forecasting 24

Introducing spectral attention for long-range dependency in ...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Bong Gyun Kang Dongjun Lee HyunGi Kim DoHyun Chung Sungroh Yoon Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Electrical and Computer Engineering Seoul National University Department of Future Automotive Mobility Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University and Department of Electrical and Computer Engineering Seoul National University

ISBN: (纸本)9798331314385

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture longrange dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results.

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