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9页

Continuous Optimization of a Hierarchical Bayesian Network for Friedreich's Ataxia Severity Classification. 层次贝叶斯网络对friedrich共济失调严重程度分类的连续优化。

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/EMBC53108.2024.10781628

Sahan Dissanayake, Ragil Krishna, Pubudu N Pathirana, Malcolm K Horne, David J Smulewicz, Louise A Corben

{"title":"Continuous Optimization of a Hierarchical Bayesian Network for Friedreich's Ataxia Severity Classification.","authors":"Sahan Dissanayake, Ragil Krishna, Pubudu N Pathirana, Malcolm K Horne, David J Smulewicz, Louise A Corben","doi":"10.1109/EMBC53108.2024.10781628","DOIUrl":"10.1109/EMBC53108.2024.10781628","url":null,"abstract":"Machine learning algorithms for rare disorders, such as Friedreich's Ataxia (FRDA), often suffer from a lack of data. Therefore, the ability for continuous optimization of an objective assessment model would be very useful as a clinical decision support system. In this study, we propose a Bayesian Network(BN) system for FRDA severity estimation that incorporates a Bayesian Statistical updating system to continuously improve the predictive ability while providing an easily interpretable graphical model. This can work to improve the understanding of the model by the clinician, thus creating trust in the machine learning process. Furthermore, we demonstrate that by using the updating mechanism, the BN model gives a goodness-of-fit score of 0.95, a root mean square error of 9.35 and a mean absolute error of 6.72, which outperforms other regression approaches as well as improves upon the base BN by 2% in goodness of fit, roughly 1% in RMSE and 6% in MAE.","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":"143557738","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 Bioimpedance-based Tissue Identification with Frequency Response Similarity Metrics. 基于频率响应相似度量改进生物阻抗的组织识别。

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/EMBC53108.2024.10782337

Jacob Search, Sabino Zani, Brian P Mann

{"title":"Improving Bioimpedance-based Tissue Identification with Frequency Response Similarity Metrics.","authors":"Jacob Search, Sabino Zani, Brian P Mann","doi":"10.1109/EMBC53108.2024.10782337","DOIUrl":"10.1109/EMBC53108.2024.10782337","url":null,"abstract":"Tissue identification is essential for surgeons to properly perform procedures and make informed decisions to minimize potential harm to patients. Minimally invasive surgery (MIS) offers enhanced patient safety and outcomes at the cost of lost information due to restricted vision and loss of touch, among other factors. This makes it more difficult to quickly and consistently identify tissues correctly. Bioimpedance spectroscopy (BIS) offers the potential to identify tissues using rapid measurements that leverage differences in electrical properties between tissues. However, using BIS to differentiate large sets of tissues in a singular anatomical area, such as the gastrointestinal (GI) tract, has remained a significant challenge because of the overlap of similar tissues' responses and variability between measurements. This work proposes the application of frequency response function (FRF) similarity metrics as a signal processing technique to extract new features from BIS measurements on porcine tissues. These features are then used as inputs to machine learning (ML) models that are trained on an ex vivo dataset for identification of eight different in vivo porcine abdominal tissues. The ML models using similarity metric inputs performed on par or better than models using raw measurement inputs, except for the support vector machine (SVM) models. A neural network (NN) model using a similarity metric input performed best by achieving a mean accuracy of 70.3% and F-measure of 0.716. More importantly, the similarity metrics enhanced the ability of the models to identify all tissues rather than considering tissues from similar anatomical areas as the same. Ultimately, the FRF similarity metrics are a novel approach for extracting features from BIS measurements that improved identification performance when considering both accuracy and capability of differentiating all tissues in the dataset.","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":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559621","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

Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG. 声诱发神经信号分析的方法学考虑：主动脑电图、被动脑电图和脑磁图的比较研究。

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/EMBC53108.2024.10782081

Nikola Kolbl, Konstantin Tziridis, Patrick Krauss, Achim Schilling

{"title":"Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG.","authors":"Nikola Kolbl, Konstantin Tziridis, Patrick Krauss, Achim Schilling","doi":"10.1109/EMBC53108.2024.10782081","DOIUrl":"10.1109/EMBC53108.2024.10782081","url":null,"abstract":"Analyzing and deciphering brain signals on a single trial base is the main goal of brain-computer interface (BCI) research as well as neurolinguistics. In the present study, we have evaluated the efficacy of three neuroimaging techniques-active electroencephalography (EEG), passive EEG, and magnetoencephalography (MEG)-in capturing and evaluating brain activity in response to auditory stimuli. The main goals of our research included two primary components: first, to identify ROIs, and second, to determine the appropriate number of stimulus samples needed to achieve a meaningful level of reliability. To estimate this number of measurement repetitions we performed step-wise sub-sampling combined with permutation testing. This involved a detailed comparison of event-related potentials resp. fields (ERPs, ERFs) elicited by auditory stimuli such as acoustic clicks and continuous speech. Our results show that active EEG outperformed passive EEG and MEG in sensor space. However, MEG demonstrated superior signal localization in source space. These results also highlight the complexity of developing real-time speech BCIs.","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":"143559763","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

The Effects of Robotic Exoskeleton Gait Training on Improving Walking Adaptability in Persons with MS. 机器人外骨骼步态训练对提高多发性硬化症患者行走适应能力的影响

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/EMBC53108.2024.10781725

Ghaith J Androwis, Alfonse Gaite, Amanda Engler, Guang H Yue, John DeLuca

{"title":"The Effects of Robotic Exoskeleton Gait Training on Improving Walking Adaptability in Persons with MS.","authors":"Ghaith J Androwis, Alfonse Gaite, Amanda Engler, Guang H Yue, John DeLuca","doi":"10.1109/EMBC53108.2024.10781725","DOIUrl":"10.1109/EMBC53108.2024.10781725","url":null,"abstract":"The goal of the present pilot investigation is to examine the effects of 8 weeks of supervised, over-ground gait training using a robotic exoskeleton (RE) compared with a control condition (conventional gait therapy, CGT) in persons with MS with ambulatory. Four female subjects (mean age=53 years) with relapsing-remitting MS (RRMS) participated in this study and completed a total of sixteen sessions (1-hour/session) gait training in a standard therapy gym either using a RE supervised by a physical therapist (PT) trained with RE therapy (2 subjects) or with the CGT (2 subjects) supervised by a PT. Outcome measures (obstacle avoidance, ability to track augmented cues, and average walking speed while completing these dual-tasks) were measured for both groups on a smart, instrumented treadmill (C-Mill, Motekforce, Netherland) pre- and post-intervention without the RE. Overall, individuals with MS who underwent training with RE demonstrated improved walking adaptability (obstacle avoidance and augmented cues tracking) with no adverse events during the study, and improved average walking speed post training compared to baseline.Clinical Relevance- These preliminary results from four individuals with MS suggest that gait training with robotic exoskeleton may present an effective method for improving walking adaptability and average walking speed.","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":"143560043","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

Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning. 利用机器学习将胸部CT成像和肺功能测试特征相关联，实现个性化吸入治疗。

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/EMBC53108.2024.10781590

Ethan O'Connor, Emmanuel Yangue, Yu Feng, Huimin Wu, Chenang Liu

{"title":"Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning.","authors":"Ethan O'Connor, Emmanuel Yangue, Yu Feng, Huimin Wu, Chenang Liu","doi":"10.1109/EMBC53108.2024.10781590","DOIUrl":"10.1109/EMBC53108.2024.10781590","url":null,"abstract":"Inhalation therapy is the predominant method of treatment for a variety of respiratory diseases. The effectiveness of such treatment is dependent on the accuracy of medication delivery. Thus, personalized inhalation therapy wherein inhaler designs are specifically suited to the patient's needs is highly desirable. Although computational fluid-particle dynamics (CFPD)-based simulation has demonstrated potential in advancing personalized inhalation therapy, it still requires a 3D model of the patient's respiratory system. Such a model could be constructed with computed tomography (CT) images; however, CT scans are costly and have a high risk of radiation exposure. This concern motivates this study to bridge chest CT images and pulmonary function test (PFT) data, which is noninvasive and easy to obtain. To achieve this goal, an autoencoder is leveraged to find a lower dimensional representation of the CT image; PFT data is then mapped to the encoded image using partial least squares (PLS) regression. Using the decoder in the trained autoencoder, a CT image can be reconstructed by the encoded image predicted by PFT data. This method would allow for greater accessibility to chest CT imaging without exposing patients to the potential negative effects of CT scans, significantly advancing personalized inhalation therapy for respiratory diseases. The results of preliminary experiments using a real-world dataset demonstrate promising performance with our proposed approach.","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":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560295","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

3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis. 三维多特征融合卷积网络用于阿尔茨海默病诊断。

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/EMBC53108.2024.10782006

Jiao Jiao Feng, Mao Wen Ba, Nan Li, Gang Wang

{"title":"3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.","authors":"Jiao Jiao Feng, Mao Wen Ba, Nan Li, Gang Wang","doi":"10.1109/EMBC53108.2024.10782006","DOIUrl":"10.1109/EMBC53108.2024.10782006","url":null,"abstract":"The cognitive decline caused by Alzheimer's disease (AD) is closely related to the structural changes in the hippocampus captured by structural magnetic resonance imaging (sMRI). However, current deep model research on the morphological analysis of hippocampus is mainly based on 2D MRI slices, lacking a comprehensive description of the 3D surface morphology and complex textures of the hippocampus. For this reason, we propose a two-stream multi features deep learning model that establishes a descriptive system for 3D spatial structure and morphological atrophy features on the triangular mesh of left and right hippocampus. First, we encode the triangular mesh data into the spatial structural features of the hippocampal surface. Second, considering the tubular structure of the hippocampus and the inhomogeneous morphological changes caused by AD, we introduce the thickness features and Heat Kernel Signature (HKS) features for the morphological atrophy features encoding. Third, we integrate the encoded features of adjacent faces from a macroscopic perspective into the discriminative morphological features induced by AD. Finally, driven by classification tasks, the deep learning model parameters and the discriminative features are continuously optimized, thereby improving the accuracy of AD diagnosis. Our method is evaluated based on the T 1 weighted sMRI baseline data of 269 Aβ+ AD and 437 Aβ-normal cognitively(NC) subjects collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The classification accuracy of this method for AD and NC subjects is 93.4%, the sensitivity and specificity are 92.4% and 93.8%, respectively, and the area under the ROC curve (AUC) is 98.3%.","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":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558995","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

Adaptive Impedance Matching with Fault Ride Through in Wireless Power Transfer for Implanted Medical Devices. 植入式医疗设备无线电力传输中的故障穿越自适应阻抗匹配。

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/EMBC53108.2024.10782376

Han Wu, Yufei Cai, Haolun Wu, Sultan Mahmud, Ali Nezaratizadeh, Adam Khalifa

{"title":"Adaptive Impedance Matching with Fault Ride Through in Wireless Power Transfer for Implanted Medical Devices.","authors":"Han Wu, Yufei Cai, Haolun Wu, Sultan Mahmud, Ali Nezaratizadeh, Adam Khalifa","doi":"10.1109/EMBC53108.2024.10782376","DOIUrl":"10.1109/EMBC53108.2024.10782376","url":null,"abstract":"IMDs has found widespread application across various medical fields. Wirelessly powered implants are increasingly being developed to interface with neurons due to its small size. The matching network (MN) within the wireless IMD is a crucial component influencing system efficiency. Conventional approaches using fixed-value MNs struggle to adapt to changes in parameters and environment. This research proposes an adaptive algorithm-based MN that enabels the system to automatically track the maximum rectified voltage despite variations in frequency and inductor, as well as sampling errors due to random external interference. For the first time, an active voltage limiter has been integrated into the MN to reject excess power in order to safeguard the chip, rather than dissipating it as heat. Implemented in TSMC 65nm technology, this system can operate under ±15% inductance fluctuation and ±10% frequency fluctuation at 500 MHz, enabling unusable systems to obtain sufficient power. The chosen proof-of-concept for this work is a neural stimulating IMD but this approach can extend beyond this setup.","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":"143559016","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

Low-Rank Constrained Reacquired-Navigator Reconstruction of multi-shot DWI. 多镜头DWI的低秩约束重采集导航重构。

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/EMBC53108.2024.10782950

Jiantai Zhou, Huabin Zhang, Penghui Luo, Changliang Wang, Fulang Qi, Jiaojiao Hu, Kecheng Yuan, Bensheng Qiu

{"title":"Low-Rank Constrained Reacquired-Navigator Reconstruction of multi-shot DWI.","authors":"Jiantai Zhou, Huabin Zhang, Penghui Luo, Changliang Wang, Fulang Qi, Jiaojiao Hu, Kecheng Yuan, Bensheng Qiu","doi":"10.1109/EMBC53108.2024.10782950","DOIUrl":"10.1109/EMBC53108.2024.10782950","url":null,"abstract":"The Diffusion-Weighted Imaging (DWI) requires additional acquisition of phase correction data and parallel imaging prescan data to respectively suppress artifacts caused by odd-even echo errors and motion-induced phase errors. In this study, we propose subtle modifications to the widely used spin-echo DW sequence, wherein an additional 180° radiofrequency refocusing pulse is applied after the completion of image echoes to acquire fully sampled navigator-echo data. Our proposed approach draws parallels with the dual spin-echo DW technique. However, our methodology distinguishes itself by utilizing positive and negative gradients to independently capture fully sampled navigator-echo data. Following this, we employ algorithms grounded in low-rank constraints, in conjunction with the reacquired navigator-echo data to address the two major phase errors inherent in Multi-Shot DWI (MSDWI). Simulation studies and in vivo brain imaging experiments demonstrate that this approach effectively suppresses image artifacts caused by the phase error, without the need for additional time-consuming prescans.","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":"143559448","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

Efficient Electromyography-Based Typing System: Towards a Novel Approach to HCI Text Input. 高效的基于肌电图的打字系统：迈向HCI文本输入的新方法。

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/EMBC53108.2024.10782422

Yi Wang, Youhao Wang, Ruilin Zhao, Yue Shi, Yingnan Bian

引用次数: 0

HeteroEEG: A Dual-Branch Spatial-Spectral-Temporal Heterogeneous Graph Network for EEG Classification. 异质脑电图：一种用于脑电图分类的双分支空间-频谱-时间异质图网络。

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/EMBC53108.2024.10781679

Zanhao Fu, Huaiyu Zhu, Ruohong Huan, Yi Zhang, Shuohui Chen, Yun Pan

{"title":"HeteroEEG: A Dual-Branch Spatial-Spectral-Temporal Heterogeneous Graph Network for EEG Classification.","authors":"Zanhao Fu, Huaiyu Zhu, Ruohong Huan, Yi Zhang, Shuohui Chen, Yun Pan","doi":"10.1109/EMBC53108.2024.10781679","DOIUrl":"10.1109/EMBC53108.2024.10781679","url":null,"abstract":"Given the non-Euclidean topology inherent in electroencephalogram (EEG) electrode configurations, graph-based approaches, particularly graph neural networks, have shown notable success across diverse EEG classification tasks. However, since the cerebral cortex lobes function individually and/or collaboratively across diverse tasks, there exist substantial differences between intra-lobe and inter-lobe brain intrinsic functional connectivity. Existing graph networks for EEG classification are based on homogeneous graphs, yet the nature of the cerebral cortex aligns more closely with a heterogeneous graph structure. To this end, we propose HeteroEEG for EEG classification, which to the best of our knowledge is the first to reframe the challenge of exploring EEG spatial information, especially decoupling different types of brain lobes and functional connections, as heterogeneous graph reasoning. Specifically, HeteroEEG is designed to be a dual-branch network aware of spatial, spectral, and temporal EEG features. Experimental results justify the superiority of HeteroEEG in pain and emotion recognition compared with other state-of-the-art studies. The heterogeneous graph construction of HeteroEEG may shed light on future graph-based EEG classification network design.","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":"143544738","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