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Detection of event related potentials using biologically inspired networks

机译:使用受生物启发的网络检测与事件相关的电位

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The present research was proposed to classify biosignals based on chaotic models. Recurrent networks, capable of describing data variation by the means of the interaction between internal layer neurons, were designed. The result demonstrated remarkable stability against external disturbance and the ability for extraction of the system original dynamics. Also a reduction of precision was shown in detection of synchronic regions through the data filtering process. The method dependence on structure not on frequency may explain why this phenomenon happens.
机译:提出了本研究以基于混沌模型对生物信号进行分类。设计了一种能够通过内层神经元之间的相互作用描述数据变化的递归网络。结果表明,其对外部干扰具有显着的稳定性,并具有提取系统原始动力学的能力。在通过数据过滤过程检测同步区域时,精度也有所降低。该方法对结构的依赖性而不是对频率的依赖性可以解释这种现象发生的原因。

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