RESUMO
An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-world scenarios pose a different kind of problem that could not be solved yet. Specifically, in autonomous driving-it would be beneficial if the car could identify non-verbal communication of pedestrians or other drivers, as it is a common way of communication in daily traffic. Automated identification from observation whether pedestrians or other drivers communicate through subtle cues in mimicry is an unsolved problem so far, as intent and other cognitive factors are hard to derive from observation. In contrast, communicating persons usually have clear understanding whether they communicate or not, and such information is represented in their mindsets. This work investigates whether the mental processing of faces can be identified through means of a Passive Brain-Computer Interface (pBCI). This then could be used to support the cars' autonomous interpretation of facial mimicry of pedestrians to identify non-verbal communication. Furthermore, the attentive driver can be utilized as a sensor to improve the context awareness of the car in partly automated driving. This work presents a laboratory study in which a pBCI is calibrated to detect responses of the fusiform gyrus in the electroencephalogram (EEG), reflecting face recognition. Participants were shown pictures from three different categories: faces, abstracts, and houses evoking different responses used to calibrate the pBCI. The resulting classifier could distinguish responses to faces from that evoked by other stimuli with accuracy above 70%, in a single trial. Further analysis of the classification approach and the underlying data identified activation patterns in the EEG that corresponds to face recognition in the fusiform gyrus. The resulting pBCI approach is promising as it shows better-than-random accuracy and is based on relevant and intended brain responses. Future research has to investigate whether it can be transferred from the laboratory to the real world and how it can be implemented into artificial intelligences, as used in autonomous driving.
RESUMO
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.
