脑机接口应用中运动动作和运动图像信号的分析

B. Vivekananthan, R. Nithya, B. Divya
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引用次数: 0

摘要

-脑机接口(BCI)是一种计算机化的系统,它可以获取大脑信号,提取和分类不同心理活动中的特征,并将其转换为正确的控制信号,并将其传输到外部设备。脑机接口帮助有运动障碍的人。脑机接口系统的实时应用需要对运动任务进行有效的分类。基于脑电信号的运动意象任务识别仍然是研究人员面临的挑战。提取鲁棒、互信息和判别特征并将其转化为设备命令是运动图像BCI系统面临的最大挑战。本研究旨在分析运动和运动意象分类对左手和右手运动的有效性。使用高阶统计特征定义左、右运动的运动和运动图像,这些特征被馈送给分类器SVM和随机森林分类器。使用SVM分类器对运动动作的分类准确率达到62.5%,对运动图像的分类准确率达到45.83%。使用随机森林分类器,对运动动作的分类准确率达到80.2%,对运动图像的分类准确率达到64.58%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Motor Action and Motor Imagery Signals for BCI Applications
— Brain Computer Interface ( BCI) is a computerized system that acquires brain signals, extracts and classifies features during different mental activities, and converts them into correct control signals, and transfers them to external devices. BCI helps people with motor disabilities. Real-time application of a BCI system needs an efficient classification of motor tasks. Motor Imagery task identification based on EEG signals is still challenging for researchers. Extraction of robust, mutual information and discriminative features which can be converted into device commands is the biggest challenge in Motor Imagery BCI system. This study aims to analyse the effectiveness of motor and motor imagery classification for left hand and right-hand movements. The motor and motor imagery of left and right-hand movements is defined using statistical features of a higher order that are fed to classifier SVM and Random Forest Classifier. Using SVM classifier, for motor action the classification accuracy of 62.5% was reached and for motor imagery classification accuracy of 45.83% was reached. Using random forest classifier, for motor action the classification accuracy of 80.2% was reached and for motor imagery classification accuracy of 64.58% was reached.
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