A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data
A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data
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基于 ELM 的改进运动想象 BCI 数据分类的投票优化策略
DOI：
10.1007/s12559-014-9264-1
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发表时间：
2014-09-01
影响因子：
5.4
通讯作者：
Zhang, Xuan
中科院分区：
文献类型：
--
作者：
Duan, Lijuan;Zhong, Hongyan;Zhang, Xuan
提出了一种用于脑机接口（BCI）的脑电信号分类方法。为了消除高维脑电信号中的冗余信息，减少不同类别脑电信号之间的耦合，本文采用主成分分析和线性判别分析提取表征原始脑电信号的特征。接下来，我们引入基于投票的极端学习机来分类特征。对2003年BCI竞赛的真实数据进行的实验表明，我们的分类方法在速度和准确性上优于最先进的方法。
This paper presents an approach to classifying electroencephalogram (EEG) signals for brain-computer interfaces (BCI). To eliminate redundancy in high-dimensional EEG signals and reduce the coupling among different classes of EEG signals, we use principle component analysis and linear discriminant analysis to extract features that represent the raw signals. Next, we introduce the voting-based extreme learning machine to classify the features. Experiments performed on real-world data from the 2003 BCI competition indicate that our classification method outperforms state-of-the-art methods in speed and accuracy.