Pub Date : 2025-03-10DOI: 10.1109/OJEMB.2025.3549594
W. K. Wong;Filbert H. Juwono;Catur Apriono;Ismi Rosyiana Fitri
Goal: Cardiotocograph (CTG) is a widely used device for monitoring fetal health during the labor phase. However, its interpretation remains challenging due to the complex and nonlinear nature of the data. Therefore, this paper aims to propose a reliable machine learning model for predicting fetal health. Methods: This paper introduces a state-of-the-art approach for predicting fetal health from CTG recordings (statistical features) using the Kolmogorov-Arnold Networks (KANs). KANs have recently been proposed asa powerful competitor to the conventional transfer function approach in feedforward neural networks. The proposed method leverages the powerful capabilities of KANs to model the intricate relationships within the CTG data, leading to improved classification accuracy. We validate our approach on a publicly available CTG dataset, which consists of statistical features of the acquired recordings and labeled fetal health conditions. Results: The results show that KANs outperform traditional machine learning models, achieving average classification accuracy values of 93.6% and 92.6% for two-class and three-class classification tasks, respectively. Conclusion: Our results indicate that the KAN model is particularly effective in handling the nonlinearity inherent in CTG recordings, making it a promising tool for enhancing automated fetal health assessment.
{"title":"Fetal Health Prediction From Cardiotocography Recordings Using Kolmogorov–Arnold Networks","authors":"W. K. Wong;Filbert H. Juwono;Catur Apriono;Ismi Rosyiana Fitri","doi":"10.1109/OJEMB.2025.3549594","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3549594","url":null,"abstract":"<italic>Goal:</i> Cardiotocograph (CTG) is a widely used device for monitoring fetal health during the labor phase. However, its interpretation remains challenging due to the complex and nonlinear nature of the data. Therefore, this paper aims to propose a reliable machine learning model for predicting fetal health. <italic>Methods:</i> This paper introduces a state-of-the-art approach for predicting fetal health from CTG recordings (statistical features) using the Kolmogorov-Arnold Networks (KANs). KANs have recently been proposed asa powerful competitor to the conventional transfer function approach in feedforward neural networks. The proposed method leverages the powerful capabilities of KANs to model the intricate relationships within the CTG data, leading to improved classification accuracy. We validate our approach on a publicly available CTG dataset, which consists of statistical features of the acquired recordings and labeled fetal health conditions. <italic>Results:</i> The results show that KANs outperform traditional machine learning models, achieving average classification accuracy values of 93.6% and 92.6% for two-class and three-class classification tasks, respectively. <italic>Conclusion:</i> Our results indicate that the KAN model is particularly effective in handling the nonlinearity inherent in CTG recordings, making it a promising tool for enhancing automated fetal health assessment.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"345-351"},"PeriodicalIF":2.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918772","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At The end of 2019, the novel SARS-CoV-2 virus emerged in humans, spreading rapidly and leading to the COVID-19 pandemic. The outbreak caused significant morbidity and mortality, prompting governments worldwide to implement lockdowns and masking measures, which resulted in substantial social and economic disruptions. One of the most critical challenges in controlling the virus initially was the lack of diagnostic tests [1], [2], [3]. Effective diagnostic testing is essential for detecting outbreaks and mitigating transmission by allowing for early identification and intervention [4], [5], [6], [7].
{"title":"A User-Centered Service Model for Accelerating COVID-19 Diagnostic Innovation: The RADx-rad Dx Core Approach","authors":"Melissa Ledgerwood-Lee;Alexandra Hubenko;Partha Ray;Yves Theriault;Howard Brickner;Lidia F. Vazquez;Robert Schooley;Aaron Carlin;Alex Clark;Aaron Garretson;Eliah Aronoff-Spencer","doi":"10.1109/OJEMB.2025.3568203","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3568203","url":null,"abstract":"At The end of 2019, the novel SARS-CoV-2 virus emerged in humans, spreading rapidly and leading to the COVID-19 pandemic. The outbreak caused significant morbidity and mortality, prompting governments worldwide to implement lockdowns and masking measures, which resulted in substantial social and economic disruptions. One of the most critical challenges in controlling the virus initially was the lack of diagnostic tests [1], [2], [3]. Effective diagnostic testing is essential for detecting outbreaks and mitigating transmission by allowing for early identification and intervention [4], [5], [6], [7].","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"472-479"},"PeriodicalIF":2.7,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10993406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-07DOI: 10.1109/OJEMB.2025.3548838
Tomáš Kulhánek;Kvetoslava Hošková;Jitka Feberová;Miroslav Malecha
Goal: This pilot study aims to assess accuracy in detecting patient presence or absence by using a bed sensor based on mmwave radar technology above the patient bed. Methods: Patients and healthy volunteers were observed during their presence or absence in a bed in hospital and home location. These observations were compared with data coming from bed sensor monitoring patient presence using tina.care bed sensor ASWA. Results: A total of 53 different observations were performed during the study period and the bed sensor reached accuracy of 94%, precision of 90%, sensitivity of 99% and specificity of 89% to detect presence or absence of patients in a bed. Conclusions: The sensor demonstrated strong performance in detecting patient presence in bed, with reasonable specificity and low false negatives. Further research should assess bed-exit and bed-entry events, system's accuracy in a larger cohort, its impact on patient care, and the precision of vital health parameters measured by the sensor in order to compare it with similar studies.
{"title":"Assessing the Accuracy of Bed-Occupancy With a tina.care Bed Sensor in Hospital Wards and Home Care Settings: A Pilot Study","authors":"Tomáš Kulhánek;Kvetoslava Hošková;Jitka Feberová;Miroslav Malecha","doi":"10.1109/OJEMB.2025.3548838","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3548838","url":null,"abstract":"<italic>Goal:</i> This pilot study aims to assess accuracy in detecting patient presence or absence by using a bed sensor based on mmwave radar technology above the patient bed. <italic>Methods:</i> Patients and healthy volunteers were observed during their presence or absence in a bed in hospital and home location. These observations were compared with data coming from bed sensor monitoring patient presence using tina.care bed sensor ASWA. <italic>Results:</i> A total of 53 different observations were performed during the study period and the bed sensor reached accuracy of 94%, precision of 90%, sensitivity of 99% and specificity of 89% to detect presence or absence of patients in a bed. <italic>Conclusions:</i> The sensor demonstrated strong performance in detecting patient presence in bed, with reasonable specificity and low false negatives. Further research should assess bed-exit and bed-entry events, system's accuracy in a larger cohort, its impact on patient care, and the precision of vital health parameters measured by the sensor in order to compare it with similar studies.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"420-424"},"PeriodicalIF":2.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-07DOI: 10.1109/OJEMB.2025.3549674
Donald E. Ingber
{"title":"Editorial Bringing the American Economic Flywheel to a Screeching Halt","authors":"Donald E. Ingber","doi":"10.1109/OJEMB.2025.3549674","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3549674","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"320-321"},"PeriodicalIF":2.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1109/OJEMB.2025.3548613
Malik Muhammad Arslan;Xiaodong Yang;Nan Zhao;Lei Guan;Tao Cui;Daniyal Haider
Objective: Neonatal Respiratory Distress Syndrome (NRDS) poses a significant threat to newborn health, necessitating timely and accurate diagnosis. This study introduces NDL-Net, an innovative hybrid deep learning framework designed to diagnose NRDS from chest X-rays (CXR). Results: The architecture combines MobileNetV3 Large for efficient image processing and ResNet50 for detecting complex patterns essential for NRDS identification. Additionally, a Long Short-Term Memory (LSTM) layer analyzes temporal variations in imaging data, enhancing predictive accuracy. Extensive evaluation on neonatal CXR datasets demonstrated NDL-Net's high diagnostic performance, achieving 98.09% accuracy, 97.45% precision, 98.73% sensitivity, 98.08% F1-score, and 98.73% specificity. The model's low false negative and false positive rates underscore its superior diagnostic capabilities. Conclusion: NDL-Net represents a significant advancement in medical diagnostics, improving neonatal care through early detection and management of NRDS.
{"title":"NDL-Net: A Hybrid Deep Learning Framework for Diagnosing Neonatal Respiratory Distress Syndrome From Chest X-Rays","authors":"Malik Muhammad Arslan;Xiaodong Yang;Nan Zhao;Lei Guan;Tao Cui;Daniyal Haider","doi":"10.1109/OJEMB.2025.3548613","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3548613","url":null,"abstract":"<italic>Objective:</i> Neonatal Respiratory Distress Syndrome (NRDS) poses a significant threat to newborn health, necessitating timely and accurate diagnosis. This study introduces NDL-Net, an innovative hybrid deep learning framework designed to diagnose NRDS from chest X-rays (CXR). <italic>Results:</i> The architecture combines MobileNetV3 Large for efficient image processing and ResNet50 for detecting complex patterns essential for NRDS identification. Additionally, a Long Short-Term Memory (LSTM) layer analyzes temporal variations in imaging data, enhancing predictive accuracy. Extensive evaluation on neonatal CXR datasets demonstrated NDL-Net's high diagnostic performance, achieving 98.09% accuracy, 97.45% precision, 98.73% sensitivity, 98.08% F1-score, and 98.73% specificity. The model's low false negative and false positive rates underscore its superior diagnostic capabilities. <italic>Conclusion:</i> NDL-Net represents a significant advancement in medical diagnostics, improving neonatal care through early detection and management of NRDS.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"407-412"},"PeriodicalIF":2.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10914519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal: To develop a high-performance and robust solution for neonatal sleep staging that incorporates spatial topological information and functional connectivity of the brain, which are often overlooked in existing approaches. Methods: We propose MVBNSleepNet, a multi-view brain network-based convolutional neural network. The framework integrates a multi-view brain network (MVBN) to characterize brain functional connectivity from linear temporal correlation, information-theoretic, and phase-dynamics perspectives, providing comprehensive spatial topological information. A masking mechanism is employed to enhance model robustness by simulating random dropout or low-quality signal conditions. Additionally, an attention mechanism focuses on key regions of the brain network and reveals structural brain connectivity during sleep, while a CNN module extracts spatial features from brain networks and classifies them into specific sleep stages. The model was validated on a clinical dataset of 64 neonatal EEG recordings using a leave-one-subject-out validation strategy. Results: MVBNSleepNet achieved an accuracy of 83.9% in the two-stage sleep task (sleep and wakefulness) and 76.4% in the three-stage task (active sleep, quiet sleep, and wakefulness), outperforming state-of-the-art methods. Conclusions: The proposed MVBNSleepNet provides a robust and accurate solution for neonatal sleep staging and offers valuable insights into the functional connectivity of the early neural system.
{"title":"MVBNSleepNet: A Multi-View Brain Network-Based Convolutional Neural Network for Neonatal Sleep Staging","authors":"Ligang Zhou;Minghui Liu;Xia Hu;Laishuan Wang;Yan Xu;Chen Chen;Wei Chen","doi":"10.1109/OJEMB.2025.3548002","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3548002","url":null,"abstract":"<italic>Goal:</i> To develop a high-performance and robust solution for neonatal sleep staging that incorporates spatial topological information and functional connectivity of the brain, which are often overlooked in existing approaches. <italic>Methods:</i> We propose MVBNSleepNet, a multi-view brain network-based convolutional neural network. The framework integrates a multi-view brain network (MVBN) to characterize brain functional connectivity from linear temporal correlation, information-theoretic, and phase-dynamics perspectives, providing comprehensive spatial topological information. A masking mechanism is employed to enhance model robustness by simulating random dropout or low-quality signal conditions. Additionally, an attention mechanism focuses on key regions of the brain network and reveals structural brain connectivity during sleep, while a CNN module extracts spatial features from brain networks and classifies them into specific sleep stages. The model was validated on a clinical dataset of 64 neonatal EEG recordings using a leave-one-subject-out validation strategy. <italic>Results:</i> MVBNSleepNet achieved an accuracy of 83.9% in the two-stage sleep task (sleep and wakefulness) and 76.4% in the three-stage task (active sleep, quiet sleep, and wakefulness), outperforming state-of-the-art methods. <italic>Conclusions:</i> The proposed MVBNSleepNet provides a robust and accurate solution for neonatal sleep staging and offers valuable insights into the functional connectivity of the early neural system.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"459-464"},"PeriodicalIF":2.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10914539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1109/OJEMB.2025.3546771
M. Reiser;T. Mueller;A. Breidenassel;O. Amft
Goal: We investigate the effect of source-detector geometry, including distance and angle, on the reflective photoplethysmography (PPG) signal. Methods: A porcine skin phantom was used for laboratory measurements and replicated by Monte Carlo simulations. Variations in sensor geometry were analysed. Results: Laboratory measurements and Monte Carlo simulations showed agreement for various geometry settings. With decreasing negative sensor angle, the differential path length factor and the average maximum penetration depth increases. Conclusions: Our analyses highlight the influence of source-detector geometry on the PPG DC signal. Based on our analysis of penetration depth and optical path length, the geometry effects can be transferred to the PPG AC signal too. MC simulations provide an important tool to optimise PPG performance.
{"title":"Source-Detector Geometry Analysis of Reflective PPG by Measurements and Simulations","authors":"M. Reiser;T. Mueller;A. Breidenassel;O. Amft","doi":"10.1109/OJEMB.2025.3546771","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3546771","url":null,"abstract":"<italic>Goal:</i> We investigate the effect of source-detector geometry, including distance and angle, on the reflective photoplethysmography (PPG) signal. <italic>Methods:</i> A porcine skin phantom was used for laboratory measurements and replicated by Monte Carlo simulations. Variations in sensor geometry were analysed. <italic>Results:</i> Laboratory measurements and Monte Carlo simulations showed agreement for various geometry settings. With decreasing negative sensor angle, the differential path length factor and the average maximum penetration depth increases. <italic>Conclusions:</i> Our analyses highlight the influence of source-detector geometry on the PPG DC signal. Based on our analysis of penetration depth and optical path length, the geometry effects can be transferred to the PPG AC signal too. MC simulations provide an important tool to optimise PPG performance.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"400-406"},"PeriodicalIF":2.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-05DOI: 10.1109/OJEMB.2025.3538498
José A. Salazar-Castro;Diego H. Peluffo-Ordóñez;Diego M. López
Background: Post-traumatic stress disorder (PTSD) is a psychophysiological condition caused by traumatic experiences. Its diagnosis typically relies on subjective tools like clinical interviews and self-reports. Objectives: This scoping review analyzes computational methods using EEG signal processing for PTSD diagnosis, differentiation, and therapy. It provides a comprehensive overview of the entire EEG analysis pipeline, from acquisition to statistical and machine learning techniques for PTSD diagnosis. Methods: Using the PRISMA-ScR protocol, studies published between 2013 and 2024 were reviewed from databases including Scopus, Web of Science, and PubMed. A total of 73 studies were analyzed: 52 on diagnosis, 8 on differentiation, and 15 on therapy. Results: EEG Bands and Event-Related Potentials (ERP) were the dominant techniques. The Alpha band demonstrated strong performance in diagnosis and therapy. LPP ERP was most effective for diagnosis, and P300 for differentiation. Supervised SVM models achieved the highest accuracy in diagnosis (ACC = 0.997), differentiation (ACC = 0.841), and psychotherapy (ACC = 0.78). Random Forest multimodal models integrating EEG with other modalities (e.g., ECG, GSR, Speech) achieved ACC = 0.993. Unsupervised approach is employed to cluster patients to identify PTSD subtypes or to differentiate PTSD from other mental disorders. Veterans and combatants were the primary study population, and only three studies reported open datasets. Conclusions: EEG-based methods hold promise as objective tools for PTSD diagnosis and therapy. The review identified limitations in the use of ERP, sleep characterization and full-band EEG. Broader datasets representing diverse populations are essential to mitigate bias and facilitate robust inter-model comparisons. Future research should focus on deep learning, adaptive signal decomposition, and multimodal approaches.
{"title":"Advances in Electroencephalography for Post-Traumatic Stress Disorder Identification: A Scoping Review","authors":"José A. Salazar-Castro;Diego H. Peluffo-Ordóñez;Diego M. López","doi":"10.1109/OJEMB.2025.3538498","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3538498","url":null,"abstract":"<italic>Background:</i> Post-traumatic stress disorder (PTSD) is a psychophysiological condition caused by traumatic experiences. Its diagnosis typically relies on subjective tools like clinical interviews and self-reports. <italic>Objectives:</i> This scoping review analyzes computational methods using EEG signal processing for PTSD diagnosis, differentiation, and therapy. It provides a comprehensive overview of the entire EEG analysis pipeline, from acquisition to statistical and machine learning techniques for PTSD diagnosis. <italic>Methods:</i> Using the PRISMA-ScR protocol, studies published between 2013 and 2024 were reviewed from databases including Scopus, Web of Science, and PubMed. A total of 73 studies were analyzed: 52 on diagnosis, 8 on differentiation, and 15 on therapy. <italic>Results:</i> EEG Bands and Event-Related Potentials (ERP) were the dominant techniques. The Alpha band demonstrated strong performance in diagnosis and therapy. LPP ERP was most effective for diagnosis, and P300 for differentiation. Supervised SVM models achieved the highest accuracy in diagnosis (ACC = 0.997), differentiation (ACC = 0.841), and psychotherapy (ACC = 0.78). Random Forest multimodal models integrating EEG with other modalities (e.g., ECG, GSR, Speech) achieved ACC = 0.993. Unsupervised approach is employed to cluster patients to identify PTSD subtypes or to differentiate PTSD from other mental disorders. Veterans and combatants were the primary study population, and only three studies reported open datasets. <italic>Conclusions:</i> EEG-based methods hold promise as objective tools for PTSD diagnosis and therapy. The review identified limitations in the use of ERP, sleep characterization and full-band EEG. Broader datasets representing diverse populations are essential to mitigate bias and facilitate robust inter-model comparisons. Future research should focus on deep learning, adaptive signal decomposition, and multimodal approaches.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"332-344"},"PeriodicalIF":2.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10874198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-04DOI: 10.1109/OJEMB.2025.3538256
{"title":"2024 Index IEEE Open Journal of Engineering in Medicine and Biology Vol. 5","authors":"","doi":"10.1109/OJEMB.2025.3538256","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3538256","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"885-909"},"PeriodicalIF":2.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Human movement augmentation through supernumerary effectors is an emerging field of research. However, controlling these effectors remains challenging due to issues with agency, control, and synchronizing movements with natural limbs. A promising control strategy for supernumerary effectors involves utilizing electroencephalography (EEG) through motor imagery (MI) functions. In this work, we investigate whether MI activity associated with a supernumerary effector could be reliably differentiated from that of a natural one, thus addressing the concern of concurrency. Twenty subjects were recruited to participate in a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements, conducted in a virtual reality setting. Results: A lightweight deep-learning model that accounts for the temporal, spatial and spectral nature of the EEG data is proposed and called BandFocusNet, achieving an average classification accuracy of 70.9% using the leave-one-subject-out cross validation method. The trustworthiness of the model is examined through explainability analysis, and influential regions-of-interests are cross-validated through event-related-spectral-perturbation (ERSPs) analysis. Explainability results showed the importance of the right and left frontal cortical regions, and ERSPs analysis showed an increase in the delta and theta powers in these regions during the MI of the natural thumb but not during the MI of the supernumerary thumb. Conclusion: Evidence in the literature indicates that such activation is observed during the MI of natural effectors, and its absence could be interpreted as a lack of embodiment of the supernumerary thumb.
{"title":"BandFocusNet: A Lightweight Model for Motor Imagery Classification of a Supernumerary Thumb in Virtual Reality","authors":"Haneen Alsuradi;Joseph Hong;Alireza Sarmadi;Robert Volcic;Hanan Salam;S. Farokh Atashzar;Farshad Khorrami;Mohamad Eid","doi":"10.1109/OJEMB.2025.3537760","DOIUrl":"https://doi.org/10.1109/OJEMB.2025.3537760","url":null,"abstract":"<italic>Objective:</i> Human movement augmentation through supernumerary effectors is an emerging field of research. However, controlling these effectors remains challenging due to issues with agency, control, and synchronizing movements with natural limbs. A promising control strategy for supernumerary effectors involves utilizing electroencephalography (EEG) through motor imagery (MI) functions. In this work, we investigate whether MI activity associated with a supernumerary effector could be reliably differentiated from that of a natural one, thus addressing the concern of concurrency. Twenty subjects were recruited to participate in a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements, conducted in a virtual reality setting. <italic>Results:</i> A lightweight deep-learning model that accounts for the temporal, spatial and spectral nature of the EEG data is proposed and called BandFocusNet, achieving an average classification accuracy of 70.9% using the leave-one-subject-out cross validation method. The trustworthiness of the model is examined through explainability analysis, and influential regions-of-interests are cross-validated through event-related-spectral-perturbation (ERSPs) analysis. Explainability results showed the importance of the right and left frontal cortical regions, and ERSPs analysis showed an increase in the delta and theta powers in these regions during the MI of the natural thumb but not during the MI of the supernumerary thumb. <italic>Conclusion:</i> Evidence in the literature indicates that such activation is observed during the MI of natural effectors, and its absence could be interpreted as a lack of embodiment of the supernumerary thumb.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"305-311"},"PeriodicalIF":2.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10869342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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