Christiane Glatz

Using EEG to Understand why Behavior to Auditory In-vehicle Notifications Differs Across Test Environments

In this study, we employ EEG methods to clarify why auditory notifications, which were designed for task management in highly automated trucks, resulted in different performance behavior, when deployed in two different test settings: (a) student volunteers in a lab environment, (b) professional truck drivers in a realistic vehicle simulator. Behavioral data showed that professional drivers were slower and less sensitive in identifying notifications compared to their counterparts. Such differences can be difficult to interpret and frustrates the deployment of implementations from the laboratory to more realistic settings. Our EEG recordings of brain activity reveal that these differences were not due to differences in the detection and recognition of the notifications. Instead, it was due to differences in EEG activity associated with response generation. Thus, we show how measuring brain activity can deliver insights into how notifications are processed, at a finer granularity than can be afforded by behavior alone.

Towards Adaptive Ambient In-Vehicle Displays and Interactions: Insights and Design Guidelines from the 2015 AutomotiveUI Dedicated Workshop

Informing a driver of a vehicle’s changing state and environment is a major challenge that grows with the introduction of in-vehicle assistant and infotainment systems. Even in the age of automation, the human will need to be in the loop for monitoring, taking over control, or making decisions. In these cases, poorly designed systems could lead to needless attentional demands imparted on the driver, taking it away from the primary driving task. Existing systems are offering simple and often unspecific alerts, leaving the human with the demanding task of identifying, localizing, and understanding the problem. Ideally, such systems should communicate information in a way that conveys its relevance and urgency. Specifically, information useful to promote driver safety should be conveyed as effective calls for action, while information not pertaining to safety (therefore less important) should be conveyed in ways that do not jeopardize driver attention. Adaptive ambient displays and peripheral interactions have the potential to provide superior solutions and could serve to unobtrusively present information, to shift the driver’s attention according to changing task demands, or enable a driver to react without losing the focus on the primary task. In order to build a common understanding across researchers and practitioners from different fields, we held a “Workshop on Adaptive Ambient In-Vehicle Displays and Interactions” at the AutomotiveUI‘15 conference. In this chapter, we discuss the outcomes of this workshop, provide examples of possible applications now or in the future and conclude with challenges in developing or using adaptive ambient interactions.

Use the Right Sound for the Right Job: Verbal Commands and Auditory Icons for a Task-Management System Favor Different Information Processes in the Brain

Design recommendations for notifications are typically based on user performance and subjective feedback. In comparison, there has been surprisingly little research on how designed notifications might be processed by the brain for the information they convey. The current study uses EEG/ERP methods to evaluate auditory notifications that were designed to cue long-distance truck drivers for task-management and driving conditions, particularly for automated driving scenarios. Two experiments separately evaluated naive students and professional truck drivers for their behavioral and brain responses to auditory notifications, which were either auditory icons or verbal commands. Our EEG/ERP results suggest that verbal commands were more readily recognized by the brain as relevant targets, but that auditory icons were more likely to update contextual working memory. Both classes of notifications did not differ on behavioral measures. This suggests that auditory icons ought to be employed for communicating contextual information and verbal commands, for urgent requests.

Warning Signals With Rising Profiles Increase Arousal

Auditory warnings are often used to direct a user’s attention from a primary task to critical peripheral events. In the context of traffic, in-vehicle collision avoidance systems could, for example, employ spatially relevant sounds to alert the driver to the possible presence of a crossing pedestrian. This raises the question: What is an effective auditory alert in a steering environment? Ideally, such warning signals should not only arouse the driver but also result in deeper processing of the event that the driver is being alerted to. Warning signals can be designed to convey the time to contact with an approaching object (Gray, 2011). That is, sounds can rise in intensity in accordance with the physical velocity of an approaching threat. The current experiment was a manual steering task in which participants were occasionally required to recognized peripheral visual targets. These visual targets were sometimes preceded by a spatially congruent auditory warning signal. This was either a sound with constant intensity, linearly rising intensity, or non-linearly rising intensity that conveyed time-to-contact. To study the influence of warning cues on the arousal state, different features of electroencephalography (EEG) were measured. Alpha frequency, which ranges from 7.5 to 12.5 Hz, is believed to represent different cognitive processes, in particular arousal (Klimesch, 1999). That is, greater desynchronization in the alpha frequency reflects higher levels of attention as well as alertness. Our results showed a significant decrease in alpha power for sounds with rising intensity profiles, indicating increased alertness and expectancy for an event to occur. To analyze whether the increased arousal for rising sounds resulted in deeper processing of the visual target, we analyzed the event related potential P3. It is a positive component that occurs approximately 300 ms after an event and is known to be associated with recognition performance of a stimulus (Parasuraman & Beatty, 1980). In other words, smaller P3 amplitudes indicate worse identification than larger amplitudes. Our results show that sounds with time-to-contact properties induced larger P3 responses to the targets that they cued compared to targets cued by constant or linearly rising sounds. This suggests that rising sounds with time-to-contact intensity profiles evoke deeper processing of the visual target and therefore result in better identification than events cued by sounds with linearly rising or constant intensity.

Looming Auditory Collision Warnings for Semi-Automated Driving: An ERP Study

Looming sounds can be an ideal warning notification for emergency braking. This agrees with studies that have consistently demonstrated preferential brain processing for looming stimuli. This study investigates and demonstrates that looming sounds can similarly benefit emergency braking in managing a vehicle with adaptive cruise control (ACC). Specifically, looming auditory notifications induced the faster emergency braking times relative to a static auditory notification. Next, we compare the event-related potential (ERP) evoked by a looming notification, relative to its static equivalent. Looming notifications evoke a smaller fronto-central N2 amplitude than their static equivalents. Thus, we infer that looming sounds are consistent with the visual experience of an approaching collision and, hence, induced a corresponding performance benefit. Subjective ratings indicate no significant differences in the perceived workload across the notification conditions. Overall, this work suggests that auditory warnings should have congruent physical properties with the visual events that they warn for.

Reading the mobile brain: from laboratory to real-world electroencephalography

It is increasingly viable to measure the brain activity of mobile users, as they go about their everyday business in their natural world environment. This is due to: (i) modern signal processing methods, (ii) lightweight and cost-effective measurement devices, and (iii) a better, albeit incomplete, understanding of how measurable brain activity relates to mental processes. Here, we address how brain activity can be measured in mobile users and how this contrasts with measurements obtained under controlled laboratory conditions. In particular, we will focus on electroencephalography (EEG) and will cover: (i) hardware and software implementation, (ii) signal processing techniques, (iii) interpretation of EEG measurements. This will consist of hands-on analyses of real EEG data and a basic theoretical introduction to how and why EEG works.