Volltext-Downloads (blau) und Frontdoor-Views (grau)

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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar


Author:Tihomir Rohlinger, Le Ping Peng, Tobias Gerlach, Paul Pasler, Bo Zhang, Ralf SeepoldORCiDGND, Natividad Martínez MadridORCiD, Matthias Raetsch
Parent Title (English):The 5th International Conference on Advances in Artificial Intelligence, November 20 - 22,2021, virtual, (ICAAI 2021)
Publisher:Association for Computing Machinery
Place of publication:New York
Document Type:Conference Proceeding
Year of Publication:2022
Release Date:2022/04/11
Tag:Automated Artefact Separation; Driving Simulator; CNN; Driver Drowsiness Detection
First Page:54
Last Page:64
Institutes:Institut für Angewandte Forschung - IAF
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen ACM