Using time-dependent neural networks for EEG classification
Using time-dependent neural networks for EEG classification
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DOI：
10.1109/86.895948
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发表时间：
2000-12-01
期刊：
影响因子：
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通讯作者：
Pfurtscheller, G
中科院分区：
文献类型：
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作者：
Haselsteiner, E;Pfurtscheller, G
本文比较了两种不同的神经网络拓扑结构。它们用于对来自脑机接口（BCI）的单次试验脑电图（EEG）数据进行分类。简要介绍了时间序列分类，并描述了所使用的分类器。标准多层感知器（MLP）被用作标准的分类方法。它们与有限脉冲响应（FIR）MLP进行比较。这两种结构使用FIR滤波器代替静态权重，以允许分类器内部的时间处理，给出了两种结构的理论比较，给出了三个不同主题的BCI实验结果并进行了讨论。这些结果表明，更高的性能的FIR MLP相比，标准MLP。
This paper compares two different topologies of neural networks. They are used to classify single trial electroencephalograph (EEG) data from a brain-computer interface (BCI). A short introduction to time series classification is given, and the used classifiers are described. Standard multilayer perceptrons (MLPs) are used as a standard method for classification. They are compared to finite impulse response (FIR) MLPs. which use FIR filters instead of static weights to allow temporal processing inside the classifier, A theoretical comparison of the two architectures is presented, The results of a BCI experiment with three different subjects are given and discussed. These results demonstrate the higher performance of the FIR MLP compared with the standard MLP.