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• W2896966314 abstract "Sleep monitoring with wearable electroencephalography (EEG) has recently been validated and reported in the research community. One such device is our ultra-wearable, unobtrusive, and inconspicuous in-ear EEG system, which has already been demonstrated to be next-generation solution for out-of-clinic sleep monitoring. We here provide a further proof of concept of the utility of ear-EEG in day time drowsiness monitoring in the real-world. For rigour, hypnograms are obtained from manually scored daytime nap recordings from twentythree subjects, while a complexity science feature-structural complexity extracted from scalp- and ear-EEG recordings - is used in the classification stage, in conjunction with a binary-class support vector machine (SVM). The achieved drowsiness classification accuracies range from 80.0% to 82.9% for ear-EEG, with the corresponding accuracies for scalp-EEG ranging from 86.8 % to 88.8 %. Given the notoriously difficult to classify drowsiness related changes in EEG (similar to the issues with the NREM Stage 1), this conclusively confirms the feasibility of in-ear EEG for automatic light sleep classification. This also promises a key stepping stone towards continuous, discreet, and user-friendly wearable out-of-clinic drowsiness monitoring in the real-world, with numerous applications in the monitoring the state of body and mind of pilots, train drivers, and tele-operators." @default.
• W2896966314 created "2018-10-26" @default.
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• W2896966314 date "2018-07-01" @default.
• W2896966314 modified "2023-10-09" @default.
• W2896966314 title "Automatic detection of drowsiness using in-ear EEG" @default.
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• W2896966314 doi "https://doi.org/10.1109/ijcnn.2018.8489723" @default.
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