五种神经网络方法对两种精神状态脑电信号分类的性能比较

V. Khare, J. Santhosh, S. Anand
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引用次数: 4

摘要

本文展示了五种人工神经网络(ANN)技术(a)梯度下降反传播(b) Levenberg-Marquardt (c)弹性反传播(d)共轭学习梯度反传播和(e)梯度下降反传播与运动动量的分类,用于相对于清醒放松状态的右手运动规划的性能比较。采用小波包变换(WPT)对相关脑电图信号进行特征提取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance comparison using five ANN methods for classification of EEG signals of two mental states
The Paper demonstrate the comparison of performance by five artificial neural network (ANN) technique (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum for classification of planning of right hand movement with respect to an awake relaxed state. Wavelet packet transform (WPT) was used for Feature extraction of the relevant electroencephalogram (EEG) signals.
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