基于径向基函数神经网络的脑电功率谱性能监测

B.P. Kirk, J. LaCourse
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引用次数: 1

摘要

警觉性持续时间长是与低唤醒水平相关的工作的主要障碍。为了提供最高水平的安全,必须监测注意力水平,特别是视觉意识。已经设计了一个离线系统,作为实时感知预测器的先例。以脑电图(EEG)作为主要预测数据,采用径向基函数网络对注意力水平进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance monitoring from the EEG power spectrum with a radial basis function neural network
Length of vigilance is a major obstacle in jobs associated with low levels of arousal. To provide the highest levels of safety, the level of attention, particularly visual awareness, has to be monitored. A system has been designed, offline, as a precedent to a real-time awareness predictor. The electroencephalograph (EEG) is used as the major predictive data with a radial basis function network classifying the attention level.
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