Evaluation of a dry EEG system for application of passive brain-computer interfaces in autonomous driving

© 2017 Zander, Andreessen, Berg, Bleuel, Pawlitzki, Zawallich, Krol and Gramann. 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. Fromthese 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

Passive brain–computer interfaces : A perspective on increased interactivity

Passive brain–computer interfaces (passive BCI; pBCI) have been introduced and formally defined almost a decade ago and have gained considerable attention since then. In this chapter, we clarify some points of confusion and provide a perspective on the past, present, and future of the field of passive BCI. This perspective concerns a key aspect with regard to which various pBCI-based systems differ from each other: interactivity. The more interactive a system is, the more responsive it is, the more autonomous, and the better capable of adaptation. Along these lines, we identify and describe four relevant categories of systems with varying levels of interactivity: mental state assessment, open-loop adaptation, closed-loop adaptation, and automated adaptation. We give examples of past and current research for each of these categories. The latter three are collectively introduced as neuroadaptive systems. This perspective and formal categorisation helps to highlight human–computer interaction aspects that are relevant for the design of pBCI-based systems and points to possibilities for future research and development into passive BCI, implicit interaction, and neuroadaptive technology