Tagged: eye tracking

NEDE: An Open-Source Scripting Suite for Developing Experiments in 3D Virtual Environments

Background As neuroscientists endeavor to understand the brain’s response to ecologically valid scenarios, many are leaving behind hyper-controlled paradigms in favor of more realistic ones. This movement has made the use of 3D rendering software an increasingly compelling option. However, mastering such software and scripting rigorous experiments requires a daunting amount of time and effort. New method To reduce these startup costs and make virtual environment studies more accessible to researchers, we demonstrate a naturalistic experimental design environment (NEDE) that allows experimenters to present realistic virtual stimuli while still providing tight control over the subject’s experience. NEDE is a suite of open-source scripts built on the widely used Unity3D game development software, giving experimenters access to powerful rendering tools while interfacing with eye tracking and EEG, randomizing stimuli, and providing custom task prompts. Results Researchers using NEDE can present a dynamic 3D virtual environment in which randomized stimulus objects can be placed, allowing subjects to explore in search of these objects. NEDE interfaces with a research-grade eye tracker in real-time to maintain precise timing records and sync with EEG or other recording modalities. Comparison with existing methods Python offers an alternative for experienced programmers who feel comfortable mastering and integrating the various toolboxes available. NEDE combines many of these capabilities with an easy-to-use interface and, through Unity’s extensive user base, a much more substantial body of assets and tutorials. Conclusions Our flexible, open-source experimental design system lowers the barrier to entry for neuroscientists interested in developing experiments in realistic virtual environments.

Neurally and ocularly informed graph-based models for searching 3D environments

OBJECTIVE: As we move through an environment, we are constantly making assessments, judgments and decisions about the things we encounter. Some are acted upon immediately, but many more become mental notes or fleeting impressions-our implicit ‘labeling’ of the world. In this paper, we use physiological correlates of this labeling to construct a hybrid brain-computer interface (hBCI) system for efficient navigation of a 3D environment. APPROACH: First, we record electroencephalographic (EEG), saccadic and pupillary data from subjects as they move through a small part of a 3D virtual city under free-viewing conditions. Using machine learning, we integrate the neural and ocular signals evoked by the objects they encounter to infer which ones are of subjective interest to them. These inferred labels are propagated through a large computer vision graph of objects in the city, using semi-supervised learning to identify other, unseen objects that are visually similar to the labeled ones. Finally, the system plots an efficient route to help the subjects visit the ‘similar’ objects it identifies. MAIN RESULTS: We show that by exploiting the subjects’ implicit labeling to find objects of interest instead of exploring naively, the median search precision is increased from 25% to 97%, and the median subject need only travel 40% of the distance to see 84% of the objects of interest. We also find that the neural and ocular signals contribute in a complementary fashion to the classifiers’ inference of subjects’ implicit labeling. SIGNIFICANCE: In summary, we show that neural and ocular signals reflecting subjective assessment of objects in a 3D environment can be used to inform a graph-based learning model of that environment, resulting in an hBCI system that improves navigation and information delivery specific to the user’s interests.