An end-to-end platform assembling multiple tiers is built for precisely cognizing brain activities. Being fed massive electroencephalogram (EEG) data, the time-frequency spectrograms are conventionally projected into the episode-wise feature matrices (seen as tier-1). A spiking neural network (SNN) based tier is designed to distill the principle information in terms of spike-streams from the rare features, which maintains the temporal implication in the nature of EEGs. The proposed tier-3 transposes time- and space-domain of spike patterns from the SNN; and feeds the transposed pattern-matrices into an artificial neural network (ANN, Transformer specifically) known as tier-4, where a special spanning topology is proposed to match the two-dimensional input form. In this manner, cognition such as classification is conducted with high accuracy. For proof-of-concept, the sleep stage scoring problem is demonstrated by introducing multiple EEG datasets with the largest comprising 42,560 hours recorded from 5,793 subjects. From experiment results, our platform achieves the general cognition overall accuracy of 87% by leveraging sole EEG, which is 2% superior to the state-of-the-art. Moreover, our developed multi-tier methodology offers visible and graphical interpretations of the temporal characteristics of EEG by identifying the critical episodes, which is demanded in neurodynamics but hardly appears in conventional cognition scenarios.
翻译:将大量电子脑图(EEG)数据输入大量电子脑图(EEG)数据后,时间-频率光谱图通常被投射到插件特征矩阵(Seleg-1)中。基于螺旋神经网络(SNN)的层旨在从稀有特性的螺旋流中蒸馏原则信息,这些特性维持了 EEEG 性质中的时间影响。拟议的第3级转换了SNNN 中峰值模式的最大数据集,包括时间和空间-空间-域域域;将调换模式-矩阵输入一个人工神经神经网络(ANN, 特别是变换器),称为第4级,其中提出特殊分布表层与二维输入形式匹配。以这种方式,从稀有的特性的螺旋螺旋流中提取了原则信息,维持 EEEEEG 性质。我们平台通过实验结果实现了常规特性的直径直径直的直径直径直径直径直径直径直径直的直径直径直径直径直径直径直径直径直径直到直径直径直径直径直径直径直径直径直径直的直直直直直直直直直直直直直直直的直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直的直的直直直直直直的直直的直的直的直的直直直直直直直的直的直径直径直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直的直直的直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直至直至直至直直直直至直直直直直直直直直直直直