Wireless ear EEG to monitor drowsiness.

Autor: Kaveh R; University of California Berkeley, Berkeley, CA, 94708, USA. ryankaveh@berkeley.edu., Schwendeman C; University of California Berkeley, Berkeley, CA, 94708, USA. cschwendeman@berkeley.edu., Pu L; University of California Berkeley, Berkeley, CA, 94708, USA., Arias AC; University of California Berkeley, Berkeley, CA, 94708, USA., Muller R; University of California Berkeley, Berkeley, CA, 94708, USA. rikky@berkeley.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Aug 02; Vol. 15 (1), pp. 6520. Date of Electronic Publication: 2024 Aug 02.
DOI: 10.1038/s41467-024-48682-7
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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