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. 2017 Feb 28:11:78.
doi: 10.3389/fnhum.2017.00078. eCollection 2017.

Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

Thorsten O Zander  1 , Lena M Andreessen  1 , Angela Berg  2 , Maurice Bleuel  2 , Juliane Pawlitzki  2 , Lars Zawallich  2 , Laurens R Krol  1 , Klaus Gramann  3
Affiliations

Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

Thorsten O Zander et al. Front Hum Neurosci. .

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.

Keywords: EEG; ERP; autonomous driving; passive BCI; usability.

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Figures

Figure 1
Figure 1
Overview of the used EEG system, the Brain Products actiCAP Xpress. Image courtesy of Brain Products GmbH.
Figure 2
Figure 2
The different QuickBit types provided with the actiCAP Xpress. Image courtesy of Brain Products GmbH.
Figure 3
Figure 3
Experiment timeline.
Figure 4
Figure 4
Examples for signal quality ratings on a scale from one to five. Green colored parts indicate adequate signal quality, yellow parts moderate signal quality, and red parts unacceptable signal quality.
Figure 5
Figure 5
Oddball Paradigm.
Figure 6
Figure 6
Induced Alpha Paradigm.
Figure 7
Figure 7
Shifts in electrode positions after self application in mm compared to application by investigator.
Figure 8
Figure 8
Grand average ERPs of the indoor condition (top left) and the running car condition (top right) on channel Cz. Deviant (bottom left) and standard (bottom right) ERPs in comparison between indoor and car condition.
Figure 9
Figure 9
Grand Averages of the alpha band time courses for relaxed and engaged conditions indoors and in the car. For the red and the green curve, displaying the relaxed conditions, a similar pattern starting 1 s after onset of stimulus presentation is observed. Similarities over time are also apparent for the engaged conditions, represented in the black and blue curve. Clear co-variation of indoor and in car alpha time courses for both relaxed and engaged conditions is proven by high correlation between the signals.
Figure 10
Figure 10
Shifts in electrode positions after movements of the head (A), the arms (B), and the whole body (C) in mm.
Figure 11
Figure 11
Mean score of questions about wearing comfort.
Figure 12
Figure 12
Mean alpha power in relaxed and engaged trials for individual subjects.
See this image and copyright information in PMC

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