Wavelets and ensemble of FLDs for P300 classification
Wavelets and ensemble of FLDs for P300 classification
复制标题
用于 P300 分类的 FLD 小波和集成
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
F. Sepulveda
中科院分区：
文献类型：
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作者：
M. Salvaris;F. Sepulveda
在过去的几年中，使用Wadsworth脑机接口（BCI）竞赛II和III中心提供的P300数据，对各种P300分类算法进行了评估。本文提出了一种新的P300分类方法，并与BCI competition II数据集IIb和BCI competition III数据集II的最新结果进行了比较。该分类方法包括离散小波变换预处理和fisher线性判别集合进行分类。所提出的方法的性能与BCI竞争II数据集的最先进方法一样好，仅比BCI竞争III数据集的最先进方法略差。此外，所提出的方法远比目前最先进的方法计算成本低，并且可以对在线系统中的自适应行为进行修改。
Over the last few years various P300 classification algorithms have been assessed using the P300 data provided by the Wadsworth center for brain-computer interface (BCI) competitions II and III. In this paper a novel method of P300 classification is presented and compared to the state of the art results obtained for BCI competition II data set IIb and BCI competition III data set II. The novel classification method includes discrete-wavelet transform (DWT) preprocessing and an ensemble of Fishers Linear Discriminants for classification. The performance of the proposed method is as good as the state of the art method for the BCI competition II data set and only slightly worse than the state of the art method for BCI competition III data sets. Furthermore the proposed method is far less computationally expensive than the current state of the art method and could be modified for adaptive behavior in an online system.