  Neural wearables can enable life-saving drowsiness and health monitoring for
pilots and drivers. While existing in-cabin sensors may provide alerts,
wearables can enable monitoring across more environments. Current neural
wearables are promising but most require wet-electrodes and bulky electronics.
This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness
with compact hardware. The employed system integrates additive-manufacturing
for dry, user-generic earpieces, existing wireless electronics, and offline
classification algorithms. Thirty-five hours of electrophysiological data were
recorded across nine subjects performing drowsiness-inducing tasks. Three
classifier models were trained with user-specific, leave-one-trial-out, and
leave-one-user-out splits. The support-vector-machine classifier achieved an
accuracy of 93.2% while evaluating users it has seen before and 93.3% when
evaluating a never-before-seen user. These results demonstrate wireless, dry,
user-generic earpieces used to classify drowsiness with comparable accuracies
to existing state-of-the-art, wet electrode in-ear and scalp systems. Further,
this work illustrates the feasibility of population-trained classification in
future electrophysiological applications.
