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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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Decade-long performance of constructed wetlands in the dairy industry following an aerated facultative pond

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Chemosphere 2025年 376卷 144271页

作者： Vieira de Mendonça, Henrique Carvalho, Camila da Motta de Costa, Anderson Gomide Ribas, Maria Clara Euriques, Jorge Felipe Post-graduate Program in Agricultural and Environmental Engineering / Institute of Technology / Engineering Department Federal Rural University of Rio de Janeiro Campus Seropédica Seropédica RJ Rio de Janeiro23890-000 Brazil

Long-term data are essential for decision-making in the operation of constructed wetlands;however, such data are scarce. In the present study, a subsurface flow CW system was monitored over a 10-year period for the treatment of wastewater from the dairy industry. Prior to the CW, an aerated facultative lagoon was operated, and its data were also included in the study. The integration of both natural treatment systems (Lagoon + CW) revealed synergy in the combined removal of pollutants and nutrients. Up until the seventh year of operation, the integration of the systems removed more than 98% of COD, BOD5, and TSS, as well as 99.8%, 96.5%, and 71.3% of oils and greases, ammoniacal nitrogen, and total phosphorus, respectively. Surfactants, oils, and greases were entirely removed. The presence of significant clogging and surface flow from the seventh year onwards led to a reduction in treatment efficiency, indicating that this is the maximum recommended operating time for CWs following an aerated facultative lagoon in the dairy industry. After this period, all substrates should be replaced. © 2025 Elsevier Ltd

关键词： Agroindustry Brachiaria Environmental control Pollutant removal Synergy

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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CapST: Leveraging Capsule Networks and Temporal Attention for Accurate Model Attribution in Deep-fake Videos

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷

作者： Ahmad, Wasim Peng, Yan-Tsung Chang, Yuan-Hao Ganfure, Gaddisa Olani Khan, Sarwar Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science Dire Dawa University Dire Dawa Ethiopia Social Networks and Human-Centered Computing Taiwan International Graduate Program Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan

Deep-fake videos, generated through AI face-swapping techniques, have garnered considerable attention due to their potential for impactful impersonation attacks. While existing research primarily distinguishes real from fake videos, attributing a deep-fake to its specific generation model or encoder is crucial for forensic investigation, enabling precise source tracing and tailored countermeasures. This approach not only enhances detection accuracy by leveraging unique model-specific artifacts but also provides insights essential for developing proactive defenses against evolving deep-fake techniques. Addressing this gap, this article investigates the model attribution problem for deep-fake videos using two datasets-Deepfakes from Different Models (DFDM) and GANGen-Detection, which comprise deep-fake videos and images generated by GAN models. We select only fake images from the GANGen-Detection dataset to align with the DFDM dataset, which specifies the goal of this study, focusing on model attribution rather than real/fake classification. This study formulates deep-fake model attribution as a multiclass classification task, introducing a novel Capsule-Spatial-Temporal (CapST) model that effectively integrates a modified VGG19 (utilizing only the first 26 out of 52 layers) for feature extraction, combined with Capsule Networks and a Spatio-Temporal attention mechanism. The Capsule module captures intricate feature hierarchies, enabling robust identification of deep-fake attributes, while a video-level fusion technique leverages temporal attention mechanisms to process concatenated feature vectors and capture temporal dependencies in deep-fake videos. By aggregating insights across frames, our model achieves a comprehensive understanding of video content, resulting in more precise predictions. Experimental results on the DFDM and GANGen-Detection datasets demonstrate the efficacy of CapST, achieving substantial improvements in accurately categorizing deep-fake videos

关键词： Computer forensics

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Valorization of Spondias bahiensis waste: Ultrasound-assisted extraction, in vitro phenolics bioaccessibility, and effect on the stability of lipid-protein oxidation in pre-cooked goat meatballs

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Food Bioscience 2025年 69卷

作者： da Silva Ferreira, Valquíria Cardoso de Santana Neto, Deocleciano Cassiano Fernandes Santos, Miriane Moreira da Silva, Anna Júllia Bezerra de Azevedo Moura, Gleyce Swellen Santos Ribeiro, José Evangelista Lima, Marcos dos Santos Magnani, Marciane Silva, Fábio Anderson Pereira da Department of Management and Agro-industrial Technology Federal University of Paraíba Paraíba Bananeiras Brazil Post-graduate Program in Food Engineering Department of Chemical Engineering Federal University of Paraná Brazil Post-graduate Program in Food Science and Technology Department of Food Engineering Technology Center Federal University of Paraíba Paraíba Joao Pessoa 58051-900 Brazil Department of Food Engineering Technology Center Federal University of Paraíba Paraíba Joao Pessoa 58051-900 Brazil

This study aimed to optimize the ultrasound-assisted extraction of phenolic compounds from the bagasse of umbu-cajá (Spondias bahiensis), a native Brazilian fruit, and to assess their bioaccessibility. Additionally, the influence of different concentrations of the umbu-cajá bagasse (SBB) extract on the oxidative stability of pre-cooked goat meatballs was investigated over a 10-day refrigerated storage period by quantifying lipid oxidation products. A central composite rotational design determined the optimal ethanol concentration, sonication time, and bagasse-solvent ratio. The optimized extract was applied to meatballs at two concentrations: EX2 (meatballs with 7.0 g/100 g of added SBB extract) and EX4 (meatballs with 13.7 g/100 g of added SBB extract). A control treatment (no antioxidant) and BHT-treated meatballs (0.02 g/100 g) were included as references. Meatballs were evaluated by measuring instrumental color, peroxide value, TBARS, carbonyl compounds, and through simulated digestion. Optimal extraction conditions were 55 % ethanol, 20 min of sonication, and a 1:36.8 bagasse-to-solvent ratio (m/v). The extract showed high antioxidant activity and bioaccessibility of catechin (1243.75 %), epicatechin gallate (918.75 %), procyanidins B2 (915.38 %), gallic acid (319.76 %) and procyanidins B1 (136.36 %). SBB extract slowed lipid oxidation until day 10 and protein oxidation until day 5 but did not inhibit color change. The meatballs had low content of oxidative compounds and high antioxidant capacity in the intestinal fraction, indicating that SBB extract can partially or totally replace the synthetic preservative. © 2025

关键词： Antioxidant Goat meat In vitro gastrointestinal digestion Oxidation Phenolic compounds Umbu-cajá

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Intelligent reflux and suction detection system for ventricular assist devices: in silico study

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Research on Biomedical Engineering 2025年第1期41卷 1-23页

作者： Santos, Bruno J. Cestari, Idágene A. Biomedical Engineering Graduate Program Polythecnic School of Engineering University of Sao Paulo Av. Prof. Luciano Gualberto Travessa 3 nº380 Sao Paulo05508-010 Brazil Department of Information Technology of Universidade São Judas Tadeu Rua Taquari nº546 Sao Paulo03166-000 Brazil Faculdade de Medicina Bioengineering Division - Heart Institute Hospital das Clinicas HCFMUSP Universidade de Sao Paulo LIM Bioengenharia Sao Paulo Sao Paulo Brazil

Introduction: Left ventricular assist devices (LVADs) serve both as a bridge to transplantation and as destination therapy for the treatment of congestive heart failure (CHF). However, the inability of the existing control strategies to automatically adapt LVAD flow according to hemodynamic changes can significantly impact patient survival and quality of life. Physiological control strategies for LVAD show promising results, with reflux and suction detection (RSD) increasing device safety. Methods: This study presents in silico results of an RSD system based on measurements of inlet and outlet pressures in continuous-flow LVAD. Two strategies were used to investigate control feasibility, safety, and adjustments to nonlinear variations and comprehensively assess the system’s state considering a structured algorithm (SA) and ensembles of AI models (eAIm): K-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). Results: The SA submodule achieved an accuracy of 99.66% in suction detection but showed limitations in reflux events, with 80.04% accuracy and an F1-Score of 70.4%. The KNN and SVM models demonstrated performance exceeding 96% for both events, exhibiting more excellent stability than the SA submodule. The ANN excelled with low variability and an RMSE of 0.07 in R1, though its suction accuracy (96.7%) was slightly lower than for reflux (99.48%). The KNN was the most effective model, achieving 99.66% accuracy in suction and 98.44% in reflux. The SVM also produced competitive results but with variability across evaluations. The eAIm model showed satisfactory precision (97.78% for suction and 97.14% for reflux), with variations depending on the scenario. The eAIm is recommended for optimization in precision-critical situations. Discussion: These strategies are designed to fulfill the proposal’s feasibility, flexibility, and safety requirements. They address the challenges of achieving consistent reproduction using an SA and the

关键词： Left ventricular assist devices

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Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils

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Computers in Biology and Medicine 2025年 184卷 109410-109410页

作者： Park, Jaehee Cheong, Da Yeon Lee, Gyudo Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and various forms of amyloidosis. Atomic force microscopy (AFM) stands as the most representative method for studying amyloid fibril morphology. However, obstacles in AFM images, including noise, salt, and amorphous aggregates, often impede accurate sample quantification. In this study, we developed denoising software employing a U-Net deep learning architecture to address this issue. The software efficiently eliminated various impediments that interfere with fibril analysis in noisy AFM images, thereby facilitating precise quantification of amyloid fibrils. We also developed automated fibril analysis technologies using the denoised AFM images, leading to quicker, more precise, and more objective assessments of fibril morphology. Furthermore, we presented a method for fibril stiffness extraction from a modulus image through mask creation based on a denoised height image. Our approach secures time efficiency and precision in analyzing amyloid morphology, and we believe it will significantly advance the currently stagnant research on amyloid-related diseases. © 2024 The Authors

关键词： Neurodegenerative diseases

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Conceptual problems in the body-space conceptualizations in A Cyborg Manifesto in the context of Sartori’s conceptual analysis

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Cogent Arts and Humanities 2025年第1期12卷

作者： Tüfenkci, Oğuzhan Koçyiğit, Rıfat Gökhan Institute of Science and Technology Department of Architecture Building Information Graduate Program Mimar Sinan Fine Arts University Fındıklı Istanbul Beyoğlu Turkey

A Cyborg Manifesto, one of the key texts of posthumanist theory, is frequently referenced in research across various fields such as science, philosophy, art, and design. The authors of this study suggest that some research using A Cyborg Manifesto as a methodological tool to ensure scientific validity may encounter challenges due to the way certain concepts within the text are formulated. With this in mind, the study focuses on architectural research that engages with the manifesto, based on the idea that conceptual inconsistencies in a source may lead to limitations in establishing scientific validity. Accordingly, the research examines how concepts are constructed within the body-space conceptualizations in the manifesto, with the hypothesis that these concepts may not have been formulated accurately. Giovanni Sartori’s method of conceptual misformation is applied, analyzing a specific argument from the manifesto’s body-space conceptualizations where conceptual inconsistencies are identified. In this analysis, issues of conceptual stretching and conceptual confusion are observed. By emphasizing the importance of clear and consistent conceptual use in architectural research, this study aims to contribute to the broader discourse on scientific knowledge production. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： A Cyborg Manifesto architecture conceptual analysis Cyberculture Gender Giovanni Sartori posthumanism Sociology & Social Policy

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The Role of technology in Climate Resilience: A Systematic Literature Review and Mapping Study Approach 8

The Role of Technology in Climate Resilience: A Systematic L...

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8th International Conference on Engineering and Applied Science, ICEAT 2024

作者： Aprizal, A. Wiranatakusuma, D.B. Rizki, M. Anugrah, R.A. Master of Economics Student Post Graduate Program Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Economics Faculty of Economics and Business Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Master of Economics Student Faculty of Business Economics and Social Development Universiti Malaysia Terengganu Terengganu Malaysia Department of Automotive Engineering Technology Vocational Program Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

Strengthening climate resilience is a global priority, essential for ensuring sustainable development and improving overall well-being. Digital technology and innovation play a pivotal role in enhancing this resilience. This paper aims to examine literature related to the role of technology in supporting climate resilience, as indexed in the Scopus database. A total of 22 articles were reviewed, employing systematic literature review and mapping study methods. The analysis covered publication sources, annual publication trends, contributing countries, research approaches, related issues, and the most cited papers, utilizing Excel 365 for data processing. Additionally, VOSviewer was used to visually analyze keyword occurrences. The review reveals a strong connection between technology and climate resilience, with digital technology being the most prominent focus. Key strategies for strengthening climate resilience include developing tools and methods that facilitate emission reductions, enhance resource efficiency, and improve climate risk management. The findings indicate a growing research trend on climate resilience, predominantly addressing carbon emissions, corporate finance, logistics, drought, asset management, and climate change mitigation. The 22 articles reviewed are categorized into various topics, with digital technology emerging as the most studied area, comprising 6 documents (27.27%). Following this, 4 papers (18.18%) explore the topic of smart agriculture. Other notable topics include energy transition and smart cities, each with 3 papers (13.64%). Additional research themes include green innovation, renewable energy sources, rural water providers, and the role of higher education institutions. Advancing climate resilience necessitates leveraging advanced technologies such as big data, artificial intelligence (AI), the Internet of Things (IoT), and digital twins. These technologies enable real-time analysis and environmental monitoring, fostering bett

关键词： Risk management

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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