EEG discrimination using wavelet packet transform and a reduced-dimensional recurrent neural network
EEG discrimination using wavelet packet transform and a reduced-dimensional recurrent neural network
复制标题
使用小波包变换和降维递归神经网络进行脑电图鉴别
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
T. Tsuji
中科院分区：
文献类型：
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作者：
N. Bu;K. Shima;T. Tsuji
提出了一种新的降维递归神经网络用于脑电（EEG）识别。由于脑电信号的时变特性，递归神经网络是一种有效的脑电信号模式识别方法。然而，当处理高维数据时，神经网络通常存在计算负担重和训练困难的问题。为了克服这些问题，建议神经网络采用降维阶段的网络结构的经常性概率神经网络。此外，脑电的识别方法开发使用小波包变换（WPT）和建议的神经网络。用手指运动过程中测量的脑电信号进行脑电辨别实验。实验结果表明，该方法能够获得较高的识别性能。
This paper proposes a novel reduced-dimensional recurrent neural network (NN) for electroencephalography (EEG) discrimination. Due to time-varying characteristics of EEG signals, recurrent NN is a useful approach for EEG pattern discrimination. However, when dealing with high-dimensional data, NNs usually have problems of heavy computation burden and difficulty in training. To overcome these problems, the proposed NN incorporates a dimension-reducing stage into the network structure of a recurrent probabilistic NN. Moreover, an EEG discrimination method is developed using wavelet packet transform (WPT) and the proposed NN. EEG discrimination experiments were conducted with EEG signals measured during finger movements. The experimental results of four subjects indicate that the proposed method can achieve relatively high discrimination performance.
DOI：
10.1016/s0013-4694(97)00022-2
发表时间：
1997-09-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
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作者：
McFarland, DJ;McCane, LM;Wolpaw, JR
通讯作者：
Wolpaw, JR