Tagged: Computer vision

Using single-trial EEG to estimate the timing of target onset during rapid serial visual presentation

The timing of a behavioral response, such as a button press in reaction to a visual stimulus, is highly variable across trials. In this paper we describe a methodology for single-trial analysis of electroencephalography (EEG) which can be used to reduce the error in the estimation of the timing of the behavioral response and thus reduce the error in estimating the onset time of the stimulus. We consider a rapid serial visual presentation (RSVP) paradigm consisting of concatenated video clips and where subjects are instructed to respond when they see a predefined target. We show that a linear discriminator, with inputs distributed across sensors and time and chosen via an information theoretic feature selection criterion, can be used in conjunction with the response to yield a lower error estimate of the onset time of the target stimulus compared to the response time. We compare our results to response time and previous EEG approaches using fixed windows in time, showing that our method has the lowest estimation error. We discuss potential applications, specifically with respect to cortically-coupled computer vision based triage of large image databases

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.

In a Blink of an Eye and a Switch of a Transistor: Cortically-coupled Computer Vision

Our society’s information technology advancements have resulted in the increasingly problematic issue of information overload-i.e., we have more access to information than we can possibly process. This is nowhere more apparent than in the volume of imagery and video that we can access on a daily basis-for the general public, availability of YouTube video and Google Images, or for the image analysis professional tasked with searching security video or satellite reconnaissance. Which images to look at and how to ensure we see the images that are of most interest to us, begs the question of whether there are smart ways to triage this volume of imagery. Over the past decade, computer vision research has focused on the issue of ranking and indexing imagery. However, computer vision is limited in its ability to identify interesting imagery, particularly as ¿interesting¿ might be defined by an individual. In this paper we describe our efforts in developing brain-computer interfaces (BCIs) which synergistically integrate computer vision and human vision so as to construct a system for image triage. Our approach exploits machine learning for real-time decoding of brain signals which are recorded noninvasively via electroencephalography (EEG). The signals we decode are specific for events related to imagery attracting a user’s attention. We describe two architectures we have developed for this type of cortically coupled computer vision and discuss potential applications and challenges for the future