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.

该研究提出了一种多层次的神经网络框架，能够高准确率地进行神经认知，在睡眠阶段评分上超过了现有技术，同时也提供了可视化的脑电波信号特点解释方法。