用于脑电信号分类的快速动态时间翘曲特征提取

Hiram Calvo, J. Paredes, J. Figueroa-Nazuno
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引用次数: 1

摘要

本文采用快速动态时间扭曲算法(FDTW)对18组脑电记录进行特征提取。每组包含150个刺激事件,旨在研究“马-羊”、“SWING - MELON”等具体物体名词对之间的语义关系,以及这种关系活动如何在脑电图信号中反映出来。基于后者,训练了不同的分类器,以便将一组信号与先前学习的人类答案相关联,属于两类:语义相关或不语义相关。对比其他3种特征提取方法,并使用5种不同的分类算法对分类精度进行评价。在所有情况下,使用FDTW而不是LPC、PCA或ICA进行特征提取,都有利于分类精度的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast Dynamic Time Warping Feature Extraction for EEG Signal Classification
In this work the fast algorithm Dynamic Time Warp (FDTW) is used as a method of feature extraction for 18 sets of EEG records. Each set contains 150 events of stimulation designed to study the semantic relationship between pairs of nouns of concrete objects such as "HORSE - SHEEP" and "SWING - MELON" and how this relationship activity is reflected in EEG signals. Based on these latter, different classifiers were trained in order to associate a set of signals to a previously learned human answer, pertaining to two classes: semantically related, or not semantically related. The results of classification accuracy were evaluated comparing with other 3 methods of feature extraction, and using 5 different classification algorithms. In all cases, classification accuracy was benefited from using FDTW instead of LPC, PCA or ICA for feature extraction.
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