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Adaptive neural network filter for visual evoked potential estimation

机译:自适应神经网络滤波器用于视觉诱发电位估计

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摘要

The authors describe a new approach to enhance the signal-to-noise-ratio (SNR) of visual evoked potential (VEP) based on an adaptive neural network filter. Neural networks are usually used in an nonadaptive way. The weights in the neural network are adjusted during training but remain constant in actual use. Here, the authors use an adaptive neural network filter with adaptation capabilities similar to those of the traditional linear adaptive filter and suitable training scheme is also examined. In contrast with linear adaptive filters, adaptive neural network filters possess nonlinear characteristics which can better match the nonlinear behaviour of evoked potential signals. Simulations employing VEP signals obtained experimentally confirm the superior performance of the adaptive neural network filter against traditional linear adaptive filter.
机译:作者介绍了一种基于自适应神经网络滤波器的增强视觉诱发电位(VEP)的信噪比(SNR)的新方法。神经网络通常以非自适应方式使用。神经网络中的权重在训练期间进行调整,但在实际使用中保持不变。在这里,作者使用了具有与传统线性自适应滤波器相似的自适应能力的自适应神经网络滤波器,并且还研究了合适的训练方案。与线性自适应滤波器相比,自适应神经网络滤波器具有非线性特性,可以更好地匹配诱发电位信号的非线性行为。通过实验获得的使用VEP信号进行的仿真实验证实了自适应神经网络滤波器相对于传统线性自适应滤波器的优越性能。

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