基于支持向量机框架的运动想象脑电分类
lassification of Motor Imagery by means of SVM Framework
一个脑-机接口（brain-computer interface, BCI）系统需要有效的在线处理脑电信号以便进行实时的大脑活动状态分类。在本文中我们提出一种基于支持向量机框架的单次脑电分类方法以区分左右手运动想象活动。我们通过小波变换（Wavelet Transform, WT）提取了两个频段（μ和β节律）脑电数据的时频特征信息，然后采用基于支持向量机（Support Vector Machines, SVM）框架的方法进行以前判别信息的累积以便于预测使用者的意动方向。该方法改善了单次在线分类的准确率和稳定性，达到了仅10％的错误率。
brain-computer interface (BCI) system requires effective online processing of electroencephalogram (EEG) signals for a real-time realization of continuous classifying brain activity. In this work, we present a framework for single trial online classification of imaginary left and right hand movements which is based on support vector machines (SVM). For classification of motor imagery, the time-frequency information is extracted from two frequency bands (μ and β rhythms) of EEG data with Morlet wavelets, and the SVM framework is used for accumulation of the discrimination evidence over time to infer user’s unknown motor intention. This algorithm improved the single trial online classification accuracy as well as stability, and achieved a low classification error rate of 10%.
廖祥、尧德中、吴丹、尹愚
生物科学现状、生物科学发展生物科学研究方法、生物科学研究技术计算技术、计算机技术
脑-机接口，支持向量机框架，小波，单次脑电分类，运动想象
brain-computer interface SVM framework wavelet single trial classification motor imagery
廖祥,尧德中,吴丹,尹愚.基于支持向量机框架的运动想象脑电分类[EB/OL].(2005-12-08)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/200512-184.点此复制
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