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Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

机译:卷积神经网络提取ERP系统的时空特征

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摘要

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.
机译:事件相关电位(ERP)的特征尚未完全理解,文盲问题仍未解决。为此,在大多数脑机接口应用程序中,P300峰值已被用作ERP的功能,但是没有出现此峰值的受试者很常见。卷积神经网络的最新发展提供了一种分析ERP的时空特征的方法。在这里,我们训练具有2个卷积层的卷积神经网络,其特征图表示事件相关电位的时空特征。我们发现非文盲受试者的ERP显示枕叶和顶叶之间的高度相关性,而文盲受试者仅显示额叶和中央叶的神经活动之间的相关性。非文盲在P300,P500和P700中显示峰值,而文盲则在P700附近显示峰值。 P700在这两个科目中都很强。我们发现P700高峰可能是ERP的关键特征,因为它出现在文盲和非文盲受试者中。

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