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一种基于分形维数作为特征和Adaboost作为分类器的脑机接口研究新方法。

A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier.

作者信息

Boostani Reza, Moradi Mohammad Hassan

机构信息

Amir Kabir University of Technology, Faculty of Biomedical Engineering, Tehran, Iran.

出版信息

J Neural Eng. 2004 Dec;1(4):212-7. doi: 10.1088/1741-2560/1/4/004. Epub 2004 Nov 17.

Abstract

High rate classification of imagery tasks is still one of the hot topics among the brain computer interface (BCI) groups. In order to improve this rate, a new approach based on fractal dimension as feature and Adaboost as classifier is presented for five subjects in this paper. To have a comparison, features such as band power, Hjorth parameters along with LDA classifier have been taken into account. Fractal dimension as a feature with Adaboost and LDA can be considered as alternative combinations for BCI applications.

摘要

图像任务的高速分类仍然是脑机接口(BCI)研究团队中的热门话题之一。为了提高这一分类速度,本文针对五名受试者提出了一种基于分形维数作为特征、Adaboost作为分类器的新方法。为了进行比较,还考虑了诸如频段功率、Hjorth参数等特征以及线性判别分析(LDA)分类器。分形维数作为一种特征与Adaboost和LDA相结合,可以被视为脑机接口应用的替代组合。

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