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ARTIFICIAL NEURAL NETWORKS TO EXTRACT KNOWLEDGE FROM EEG

机译:人工神经网络从脑电图中提取知识

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EEG signals are very difficult to interpret because they are dynamic, non-linear and non-stationary signals. Human expertise also indicates that multi-level analysis must be performed to integrate various sources of knowledge. In this paper, we review these difficulties and propose that artificial neural networks could be good candidates to handle such a difficult problem.
机译:脑电信号很难解释,因为它们是动态的,非线性的和非平稳的信号。人类的专业知识还表明,必须执行多级分析以整合各种知识来源。在本文中,我们回顾了这些困难,并提出了人工神经网络可以很好地解决这一难题。

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