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多域特征融合的脑电信号肢体运动特征提取与动作识别

Extraction of body movement features and action recognition based on Multi-Domain feature fusion in electroencephalogram signals
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摘要 在脑电信号的肢体运动想象特征分类识别中,融合不同域特征提取时,存在动作识别准确度不高的问题。针对此问题,本文依据多通道采集中肢体运动想象脑电特征的复杂不同域关系,设计了用于识别肢体动作的脑电-对称正定网络运动特征分类模型,有效提取并融合不同域特征,实现了基于脑电信号的肢体特征分类以及动作的有效识别。实验结果表明,在识别四类肢体是否运动的运动想象数据集BCI Competition IV 2a上,基于所构建的分类模型在动作识别时的准确率达到0.85,Kappa系数达到0.80,具有较高精度。 In the classification and recognition of motor imagery EEG features for limb movements,there exists a problem of low action recognition accuracy when fusing features from different domains.To address this issue,this study designs an EEG-symmetric positive definite network model for motor feature classification,tailored to the complex cross-domain relationships of motor imagery EEG features in multi-channel data collection.This model effectively extracts and integrates features from different domains,achieving accurate classification of limb features and action recognition based on EEG signals.Experimental results demonstrate that on the BCI Competition IV 2a dataset,which contains motor imagery data of four types of limb movements,the proposed classification model achieves an action recognition accuracy of 0.85 and a Kappa coefficient of 0.80,indicating high precision.
作者 肖健 党选举 Xiao Jian;Dang Xuanju(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China;Key Laboratory of Guangxi College Intelligent Comprehensive Automation,Guilin 541004,China)
出处 《电子测量技术》 北大核心 2024年第18期23-30,共8页 Electronic Measurement Technology
基金 国家自然科学基金(62263004) 广西重点研发计划项目(桂科AB23075102)资助。
关键词 脑机接口 运动想象 黎曼几何 小波包分解 神经网络 brain-computer interface motor imagination Riemannian geometry wavelet packet decomposition neural network
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