Neurally and Ocularly Informed Graph-Based Models for Searching 3D Environments

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 03, 2014
Accession Number
ADA611793

Entities

People

  • Brent J. Lance
  • David C. Jangraw
  • Jun Wang
  • Paul Sajda
  • Shih-fu Chang

Organizations

  • Columbia University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Bayesian Networks
  • Brain
  • Computational Science
  • Computer Science
  • Computer Vision
  • Computers
  • Detection
  • Electrical Engineering
  • Human Factors Engineering
  • Human-Machine Interaction
  • Information Science
  • Machine Learning
  • Navigation
  • Neural Engineering
  • Precision
  • Three Dimensional
  • Virtual Reality

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks