  Driver vigilance estimation is an important task for transportation safety.
Wearable and portable brain-computer interface devices provide a powerful means
for real-time monitoring of the vigilance level of drivers to help with
avoiding distracted or impaired driving. In this paper, we propose a novel
multimodal architecture for in-vehicle vigilance estimation from
Electroencephalogram and Electrooculogram. To enable the system to focus on the
most salient parts of the learned multimodal representations, we propose an
architecture composed of a capsule attention mechanism following a deep Long
Short-Term Memory (LSTM) network. Our model learns hierarchical dependencies in
the data through the LSTM and capsule feature representation layers. To better
explore the discriminative ability of the learned representations, we study the
effect of the proposed capsule attention mechanism including the number of
dynamic routing iterations as well as other parameters. Experiments show the
robustness of our method by outperforming other solutions and baseline
techniques, setting a new state-of-the-art. We then provide an analysis on
different frequency bands and brain regions to evaluate their suitability for
driver vigilance estimation. Lastly, an analysis on the role of capsule
attention, multimodality, and robustness to noise is performed, highlighting
the advantages of our approach.
