Abstract
We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset's usability and wearing comfort.
Highlights
Driving has become a part of everyday life, which makes the drive to work or for recreational activities a simple routine task
Concluding in brief, the EEG system allowed for technically sound recordings, even with car-induced interferences present
It appears to be suitable for passive BCIs in autonomous driving scenarios, allowing mental states to be detected in real time
Summary
Driving has become a part of everyday life, which makes the drive to work or for recreational activities a simple routine task. The effects of the mental workload and effort required by driving often go unnoticed. A study by Borghini et al (2014) found that mental workload, fatigue, and drowsiness are significantly increased while driving. The field of automotive human factors and ergonomics is concerned with minimizing safety risks depending on human performance in driving tasks. A different approach aims to fully or at least partly automate the task of driving, so the human driver can be eliminated as a risk factor in most instances. The scientific field working toward this goal is called Autonomous Driving (Franke et al, 1998) and has grown more important over the past years
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