Peltoranta M, Pfurtscheller G
Department of Medical Informatics, University of Technology, Graz, Austria.
Med Biol Eng Comput. 1994 Mar;32(2):189-96. doi: 10.1007/BF02518917.
Classification of non-averaged task-related EEG responses with different types of classifier, including self-organising feature map and learning vector quantiser, K-mean, back-propagation and a combination of the last two, is reported. EEG data are collected from approximately one second periods prior to movement of the right or left index finger. A cue stimulus indicating which hand to use is employed. Feature vectors are formed by concatenating spatial information from different EEG electrodes and temporal information from different time incidents during the planning of hand movement. Power values of the most reactive frequencies within the extended alpha-band (5-16 Hz) are used as features. The features are derived from an autoregressive model fitted to the EEG signals. The performance of the classifiers and their ability to learn and generalise is tested with 200 arbitrarily selected event-related EEG data from a normal subject. Classification accuracies as high as 85-90% are achieved with the methods described here. A comparison of the classifiers is made.
报告了使用不同类型分类器（包括自组织特征映射和学习向量量化器、K均值、反向传播以及后两者的组合）对非平均任务相关脑电图反应进行分类的情况。脑电图数据是在右或左食指运动前约一秒的时间段内收集的。采用了指示使用哪只手的提示刺激。在手部运动规划过程中，通过连接来自不同脑电图电极的空间信息和来自不同时间点的时间信息来形成特征向量。扩展α波段（5 - 16赫兹）内反应最强烈频率的功率值用作特征。这些特征源自拟合脑电图信号的自回归模型。使用来自一名正常受试者的200个任意选择的事件相关脑电图数据测试了分类器的性能及其学习和泛化能力。使用此处所述方法可实现高达85% - 90%的分类准确率。对分类器进行了比较。
