  Driver drowsiness electroencephalography (EEG) signal monitoring can timely
alert drivers of their drowsiness status, thereby reducing the probability of
traffic accidents. Graph convolutional networks (GCNs) have shown significant
advancements in processing the non-stationary, time-varying, and non-Euclidean
nature of EEG signals. However, the existing single-channel EEG adjacency graph
construction process lacks interpretability, which hinders the ability of GCNs
to effectively extract adjacency graph features, thus affecting the performance
of drowsiness monitoring. To address this issue, we propose an edge-end
lightweight dual graph convolutional network (LDGCN). Specifically, we are the
first to incorporate neurophysiological knowledge to design a Baseline
Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver
drowsiness status. Additionally, to express more features within limited EEG
data, we introduce the Augmented Graph-level Module (AGM). This module captures
global and local information at the graph level, ensuring that BDSAG features
remain intact while enhancing effective feature expression capability.
Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we
utilize Adaptive Pruning Optimization (APO) on both channels and neurons,
reducing inference latency by almost half. Experiments on benchmark datasets
demonstrate that LDGCN offers the best trade-off between monitoring performance
and hardware resource utilization compared to existing state-of-the-art
algorithms. All our source code can be found at
https://github.com/BryantDom/Driver-Drowsiness-Monitoring.
