Error related negativity in observing interactive tasks

Error Related Negativity is triggered when a user either makes a mistake or the application behaves differently from their expectation. It can also appear while observing another user making a mistake. This paper investigates ERN in collaborative settings where observing another user (the executer) perform a task is typical and then explores its applicability to HCI. We first show that ERN can be detected on signals captured by commodity EEG headsets like an Emotiv headset when observing another person perform a typical multiple-choice reaction time task. We then investigate the anticipation effects by detecting ERN in the time interval when an executer is reaching towards an answer. We show that we can detect this signal with both a clinical EEG device and with an Emotiv headset. Our results show that online single trial detection is possible using both headsets during tasks that are typical of collaborative interactive applications. However there is a trade-off between the detection speed and the quality/prices of the headsets. Based on the results, we discuss and present several HCI scenarios for use of ERN in observing tasks and collaborative settings

Measuring Cognitive Conflict in Virtual Reality with Feedback-Related Negativity

As virtual reality (VR) emerges as a mainstream platform, designers have started to experiment new interaction techniques to enhance the user experience. This is a challenging task because designers not only strive to provide designs with good performance but also carefully ensure not to disrupt users' immersive experience. There is a dire need for a new evaluation tool that extends beyond traditional quantitative measurements to assist designers in the design process. We propose an EEG-based experiment framework that evaluates interaction techniques in VR by measuring intentionally elicited cognitive conflict. Through the analysis of the feedback-related negativity (FRN) as well as other quantitative measurements, this framework allows designers to evaluate the effect of the variables of interest. We studied the framework by applying it to the fundamental task of 3D object selection using direct 3D input, i.e. tracked hand in VR. The cognitive conflict is intentionally elicited by manipulating the selection radius of the target object. Our first behavior experiment validated the framework in line with the findings of conflict-induced behavior adjustments like those reported in other classical psychology experiment paradigms. Our second EEG-based experiment examines the effect of the appearance of virtual hands. We found that the amplitude of FRN correlates with the level of realism of the virtual hands, which concurs with the Uncanny Valley theory