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Deep learning-based EEG detection of mental alertness states from drivers under ethical aspects

  • One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and death. Several prestigious car manufacturers are currently pursuing the aim of automated drowsiness identification to resolve this problem. The path between neuro-scientific research in connection with artificial intelligence and the preservation of the dignity of human individual’s and its inviolability, is very narrow. The key contribution of this work is a system of data analysis for EEGs during a driving session, which draws on previous studies analyzing heart rate (ECG), brain waves (EEG), and eye function (EOG). The gathered data is hereby treated as sensitive as possible, taking ethical regulations into consideration. Obtaining evaluable signs of evolving exhaustion includes techniques that obtain sleeping stage frequencies, problematic are hereby the correlated interference’s in the signal. This research focuses on a processing chain for EEG band splitting that involves band-pass filtering, principal component analysis (PCA), independent component analysis (ICA) with automatic artefact severance, and fast fourier transformation (FFT). The classification is based on a step-by-step adaptive deep learning analysis that detects theta rhythms as a drowsiness predictor in the pre-processed data. It was possible to obtain an offline detection rate of 89% and an online detection rate of 73%. The method is linked to the simulated driving scenario for which it was developed. This leaves space for more optimization on laboratory methods and data collection during wakefulness-dependent operations.

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Author of HS ReutlingenRohlinger, Tihomir; Gerlach, Tobias; Pasler, Paul; Martinez Madrid, Natividad; Rätsch, Matthias
Erschienen in:ICAAI 2021: 2021 The 5th International Conference on Advances in Artificial Intelligence (ICAAI), 20-22 November 2021, Virtual Event, United Kingdom, proceedings
Place of publication:New York
Document Type:Conference proceeding
Publication year:2021
Tag:CNN; CNN headway; ICA; automated artefact separation; driver drowsiness detection; driving simulator; ethical consideration
Page Number:11
First Page:54
Last Page:64
DDC classes:004 Informatik
Open access?:Nein
Licence (English):License Logo  Lizenzbedingungen ACM