基于时空特征的新型运动图像识别和分类方法。

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-07 DOI:10.1109/JBHI.2024.3464550
Renjie Lv, Wenwen Chang, Guanghui Yan, Wenchao Nie, Lei Zheng, Bin Guo, Muhammad Tariq Sadiq
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引用次数: 0

摘要

运动图像作为脑机接口的一种范例,在医疗康复领域具有巨大潜力。针对脑电信号的非稳态性和低信噪比带来的挑战,如何从运动图像信号中有效提取特征并进行准确识别,是运动图像脑机接口技术的重点。本文提出了一种结合功能脑网络和图卷积网络的运动图像脑电信号分类模型。首先,利用不同的脑功能连接指标构建功能脑网络,并计算图论特征,深入分析不同运动任务下脑网络的特征。然后,将构建的脑功能网络与图卷积网络相结合,用于运动图像任务的分类和识别。基于脑功能连接的分析表明,双拳任务时的功能连接强度明显高于其他运动想象任务,而实际运动时的功能连接强度普遍优于运动想象任务。在 Physionet 公共数据集上进行的实验中,所提出的模型在多主体条件下的分类准确率达到了 88.39%,明显优于传统方法。在单被试条件下,该模型有效地解决了个体差异问题,平均分类准确率达到 99.31%。这些结果表明,所提出的模型不仅在运动想象任务的分类中表现出色,而且为不同运动任务及其相应脑区的功能连接特性提供了新的见解。
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A novel recognition and classification approach for motor imagery based on spatio-temporal features.

Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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