Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献_第5页

Beyond the Game: Multimodal Emotion Recognition Before, During, and After Gameplay. 超越游戏：游戏玩法之前、期间和之后的多模态情感识别

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782547

Efstratia Ganiti-Roumeliotou, Ioannis Ziogas, Sofia B Dias, Ghada Alhussein, Herbert F Jelinek, Leontios J Hadjileontiadis

{"title":"Beyond the Game: Multimodal Emotion Recognition Before, During, and After Gameplay.","authors":"Efstratia Ganiti-Roumeliotou, Ioannis Ziogas, Sofia B Dias, Ghada Alhussein, Herbert F Jelinek, Leontios J Hadjileontiadis","doi":"10.1109/EMBC53108.2024.10782547","DOIUrl":"10.1109/EMBC53108.2024.10782547","url":null,"abstract":"In the era of Human-Computer Interaction (HCI), understanding emotional responses through multimodal signals during interactive experiences, such as serious games (SG), is of high importance. In this work, we explore emotion recognition (ER) by analyzing multimodal data from the 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE-2) dataset, including data from 76 participants engaged in dynamic gameplay and pre-post audiovisual stimulations. Utilizing features derived from electrocardiogram (ECG), electrodermal activity (EDA), accelerometer, gyroscope, game logs (GL), affect dynamics and personality traits (PT) fed in different machine learning models, our study focuses on ER, achieving state-of-the-art performance across different experimental scenarios (accuracy: 0.967 for Negative Affect in Optimal Game using Support Vector Machines). This highlights the importance of emotional states as indicators for personalized HCI. Our approach offers valuable insights to understanding the interplay between multimodal physiological signals, GL, user's emotional states and PT, which could add to the design of adaptive, affect-sensitive SG. Distinct patterns in the data are revealed, particularly emphasizing the role of ECG-Derived Respiration features and the impact of past affectivity to current emotional state.Clinical relevance-By introducing innovative perspectives in affect-sensitive SG design, leveraging the analysis of multimodal signals, we foresee objective digital biomarkers that hold promise to broaden the clinical understanding of patients' emotional behavior during SG-based interventions.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain Generalization for Multi-disease Detection in Fundus Photographs. 眼底照片中多疾病检测的领域概化。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781556

Sarah Matta, Mathieu Lamard, Laurent Borderie, Alexandre Le Guilcher, Pascale Massin, Jean-Bernard Rottier, Beatrice Cochener, Gwenole Quellec

{"title":"Domain Generalization for Multi-disease Detection in Fundus Photographs.","authors":"Sarah Matta, Mathieu Lamard, Laurent Borderie, Alexandre Le Guilcher, Pascale Massin, Jean-Bernard Rottier, Beatrice Cochener, Gwenole Quellec","doi":"10.1109/EMBC53108.2024.10781556","DOIUrl":"10.1109/EMBC53108.2024.10781556","url":null,"abstract":"Domain generalization (DG) is a paradigm ensuring machine learning algorithms predict well on unseen domains. Recent computer vision research in DG highlighted how inconsistencies in datasets, architectures, and model criteria challenge fair comparisons. In the medical domain, the application of DG algorithms assumes an even more challenging task as medical data often exhibit significant variability due to diverse imaging modalities, patient demographics, and disease characteristics. In light of this, DG algorithms need to generalize effectively across different medical settings and patient populations for ensuring robustness and fairness in healthcare applications. In this paper, we evaluate various DG algorithms and strategies for the application of multi-disease detection in fundus photographs. We conducted extensive experiments using four heterogeneous datasets: OPHDIAT (France, diabetic population), OphtaMaine (France, general population), RIADD (India, general population) and ODIR (China, general population). The following diseases were targeted: diabetes, glaucoma, cataract, age-related macular degeneration, hypertension, myopia and other diseases/abnormalities.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality. 优化用于非突发性生命体征监测的磁感应传感器：电流控制对运行质量的影响。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782633

Mahdi Momeni, Adrian Radomski, Ulkuhan Guler, Daniel Teichmann

{"title":"Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality.","authors":"Mahdi Momeni, Adrian Radomski, Ulkuhan Guler, Daniel Teichmann","doi":"10.1109/EMBC53108.2024.10782633","DOIUrl":"10.1109/EMBC53108.2024.10782633","url":null,"abstract":"This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation. Polyp- ddpm：基于扩散的语义Polyp合成增强分割。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782077

Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao

{"title":"Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation.","authors":"Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao","doi":"10.1109/EMBC53108.2024.10782077","DOIUrl":"10.1109/EMBC53108.2024.10782077","url":null,"abstract":"This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Fréchet Inception Distance (FID) score of 78.47, compared to scores above 95.82) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6828 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Auto-labeling of Coronary Arteries on CT Coronary Angiography Images. CT冠状动脉造影图像中冠状动脉的分层自动标记。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782317

Jiangyun Li, Zhongkang Lu, Shuang Leng, Xiaohong Wang, Lohendran Baskaran, Min Sen Yew, Mark Chan, Lynette Ls Teo, Kee Yuan Ngiam, Hwee Kuan Lee, Liang Zhong, Zhiping Lin, Weimin Huang

{"title":"Hierarchical Auto-labeling of Coronary Arteries on CT Coronary Angiography Images.","authors":"Jiangyun Li, Zhongkang Lu, Shuang Leng, Xiaohong Wang, Lohendran Baskaran, Min Sen Yew, Mark Chan, Lynette Ls Teo, Kee Yuan Ngiam, Hwee Kuan Lee, Liang Zhong, Zhiping Lin, Weimin Huang","doi":"10.1109/EMBC53108.2024.10782317","DOIUrl":"10.1109/EMBC53108.2024.10782317","url":null,"abstract":"The auto-labeling of coronary artery segments plays an important role in the diagnosis of cardiovascular diseases. Due to the high degree of complexity and diversity in coronary artery structures, it is still a very challenging task after many years of exploration and study. In this paper, we propose a hierarchical scheme based on PointNet++ models and new topological structural features for automatic labeling of coronary artery segments. The inputs are 3D coronary artery centerline points extracted from CTCA images, and the outputs are the correspondent label indexes. The auto-labeling scheme include two stages: first stage is to identify the three main branches, LAD(LM), LCX and RCA. After that, utilizing the topological connectivity relationship with the three main branches, the indexes of sub-branches are identified in the second stage. We evaluated our method on a private clinical dataset. Experimental results show that the proposed method has achieved a satisfactory accuracy for clinical use.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Density Electromyography Biomarkers for Detecting and Monitoring of Spastic Muscles During Passive Stretch. 高密度肌电生物标记检测和监测痉挛肌肉被动拉伸。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781738

Nhi Nguyen, Michael Houston, Yang Liu, Yen-Ting Chen, Sheng Li, Yingchun Zhang

{"title":"High-Density Electromyography Biomarkers for Detecting and Monitoring of Spastic Muscles During Passive Stretch.","authors":"Nhi Nguyen, Michael Houston, Yang Liu, Yen-Ting Chen, Sheng Li, Yingchun Zhang","doi":"10.1109/EMBC53108.2024.10781738","DOIUrl":"10.1109/EMBC53108.2024.10781738","url":null,"abstract":"Spasticity is one of the most common symptoms that stroke patients develop after the incident. It not only leads to impaired motor control and pain but also lowers the quality of life for stroke patients. Botulinum toxin (BoNT) injection has been used as a first-line treatment for spasticity, which helps to reduce muscle tone. While the Modified Ashworth Scale (MAS) is the current clinical gold standard in evaluating spasticity, it can be affected by low inter-rater reliability. This study aims to evaluate the efficacy of high-density surface electromyography (HD-sEMG) in the passive stretch reflex pre- and post-BoNT injection as a biomarker for spasticity detection and monitoring. Ten stroke participants were recruited in this study, and the root mean squared (RMS) envelope signal and the slope between fast passive extension (FPE) and slow passive extension (SPE) were calculated. The results show that all participants have Peak RMS Envelope and Slope features lower Post-BoNT injection compared to Pre-BoNT injection (SPE: p=0.03938; FPE: p=0.00119; Slope: p=0.00143 while only five out of ten participants have their MAS Score reduce after BoNT injection (p=0.02386). These results suggest that EMG-derived features from spastic muscles may be an appropriate and quantitative alternative to the MAS score as well as a quantitative metric for detecting spasticity.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using backward adjustment with model predictive control for adaptive control of nonlinear soft artificial muscle. 将后向调节与模型预测控制相结合用于非线性柔性人工肌肉的自适应控制。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782551

Yujie Su, Disheng Xie, Jing Shu, Junming Wang, Rong Song, Kai Yu Tong

{"title":"Using backward adjustment with model predictive control for adaptive control of nonlinear soft artificial muscle.","authors":"Yujie Su, Disheng Xie, Jing Shu, Junming Wang, Rong Song, Kai Yu Tong","doi":"10.1109/EMBC53108.2024.10782551","DOIUrl":"10.1109/EMBC53108.2024.10782551","url":null,"abstract":"Soft artificial muscles possess inherent compliance and safety features, rendering them highly suitable for applications in wearable robots and unstructured environments. However, accurately modeling the nonlinearity of soft actuators proves to be a challenging task. In this paper, we present an adaptive control method that leverages model learning and model parameter backward adjustment. Our approach focuses on updating the dynamic model of the artificial muscles in two ways: by refining the input-output relation and by addressing prediction and control errors. To achieve this, we utilize tracking performance as a posterior evaluation metric for model parameter adjustment. Through a series of experiments, we demonstrate that our controller is capable of achieving reference tracking with a root mean square error (RMSE) of less than 5% across different stiffness levels. These experimental results validate the effectiveness of our proposed method in capturing the nonlinearity of soft artificial muscles, adapting to varying loads, and achieving precise reference tracking.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Custom Ankle-Foot Orthosis in Foot Drop: Influence on Body Joints Kinematics. 足下垂中定制踝关节-足矫形器：对身体关节运动学的影响。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781982

Federica Amitrano, Armando Coccia, Federico Colelli Riano, Gaetano Pagano, Vito Marsico, Giovanni D'Addio

{"title":"Custom Ankle-Foot Orthosis in Foot Drop: Influence on Body Joints Kinematics.","authors":"Federica Amitrano, Armando Coccia, Federico Colelli Riano, Gaetano Pagano, Vito Marsico, Giovanni D'Addio","doi":"10.1109/EMBC53108.2024.10781982","DOIUrl":"10.1109/EMBC53108.2024.10781982","url":null,"abstract":"Ankle Foot Orthoses (AFOs) are external supports typically prescribed in clinical practice to address foot drop deficits. Lower limb orthoses have been shown to have positive effects on spatio-temporal gait metrics, while the impact on body joint kinematics is less clear and varies in the literature. The objective of this study is to investigate the presence of a common movement pattern in body joints that compensates for the foot drop deficit. The study focuses on the trunk, knee, and hip joints. Eight patients with unilateral foot drop participated in walking trials on an instrumented treadmill. The trials included testing with both a conventional and a 3D printed custom passive AFO. The study results indicate that the use of AFOs on the impaired foot did not have a significant effect on joint kinematics in the study population, except for an improvement in trunk anterior flexion provided by the custom orthosis. There is evidence of altered biomechanics that cannot be corrected by passive orthoses alone. The study highlights the importance of physical training and long-term re-education of the patient in the correct use of the orthosis.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics. 基于深度学习的PPG频谱动脉硬度估计：一种无创心血管诊断的新方法。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782553

Kiana Pilevar Abrisham, Khalil Alipour, Bahram Tarvirdizadeh, Mohammad Ghamari

{"title":"Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics.","authors":"Kiana Pilevar Abrisham, Khalil Alipour, Bahram Tarvirdizadeh, Mohammad Ghamari","doi":"10.1109/EMBC53108.2024.10782553","DOIUrl":"10.1109/EMBC53108.2024.10782553","url":null,"abstract":"Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) - the gold standard in this field - is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving EEG-Based Cross-Subject Mental Workload Classification Performance with Euclidean-Aligned Periodic and Aperiodic Features. 利用欧几里得对齐周期和非周期特征改进基于脑电图的跨学科脑力工作负荷分类性能。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782484

Tao Wang, Yufeng Ke, Feng He, Dong Ming

{"title":"Improving EEG-Based Cross-Subject Mental Workload Classification Performance with Euclidean-Aligned Periodic and Aperiodic Features.","authors":"Tao Wang, Yufeng Ke, Feng He, Dong Ming","doi":"10.1109/EMBC53108.2024.10782484","DOIUrl":"10.1109/EMBC53108.2024.10782484","url":null,"abstract":"Enhancing the cross-subject classification performance of EEG-based mental workload (MWL) monitoring models poses a significant challenge. Traditional methods require gathering calibration data for new users to prevent performance decline. However, the calibration data collection process is time-consuming and labor-intensive. In this study, we proposed a novel cross-subject MWL classification model that does not require calibration data. Specifically, we used periodic and aperiodic components obtained through EEG spectrum decomposition as features, replacing the commonly used power spectral density (PSD) features. These features are then aligned across subjects using a modified Euclidean alignment method. Our results show that the aligned periodic and aperiodic combined features achieve the highest classification accuracy (0.791±0.077), significantly surpassing raw PSD features without alignment (0.731±0.086, p<0.05). Moreover, we found a significantly negative correlation between inter-subject distances calculated from periodic features in resting-state data and inter-subject pairwise classification accuracy (r=-0.472, p<0.001). This finding suggests a promising approach to leverage resting-state data for selecting source subjects that closely match the target subjects.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0