Multi-Tier Platform for Cognizing Massive Electroencephalogram

TL;DR

This paper presents a multi-tier platform combining spectrogram analysis, spiking neural networks, and transformers to accurately classify EEG data and interpret brain activity patterns, demonstrated on large sleep datasets.

Contribution

The novel multi-tier architecture integrates SNNs and transformers for EEG cognition, achieving higher accuracy and interpretability than existing methods.

Findings

Achieved 87% accuracy in sleep stage classification.

Outperformed state-of-the-art by 2%.

Provided graphical interpretation of EEG temporal features.

Abstract

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.