Lucas Parra

High-throughput image search via single-trial event detection in a rapid serial visual presentation task

We describe a method, using linear discrimination, for detecting single-trial EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (87 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Subjects were instructed to release a button when they recognized a target image (an image with a person/people). Trials consisted of 100 images each, with a 50% chance of a single target being in a trial. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Linear discrimination enables the estimation of a forward model and thus allows for an approximate localization of the discriminating activity. Results show multiple loci for discriminating activity (e.g. motor and visual). Using these detected EEG signatures, we show that in many cases we can detect targets more accurately than the overt response (button release) and that such signatures can be used to prioritize images for high-throughput search.

Spatial signatures of visual object recognition events learned from single-trial analysis of EEG

In this paper we use linear discrimination for learning EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (63 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Each trial consists of 100 images, with a 50% chance of a single target being in a trial. Subjects are instructed to press a left mouse button at the end of the trial if they detected a target image, otherwise they are instructed to press the right button. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Analysis of discrimination results indicated a periodic fluctuation (time-localized oscillation) in A/sub z/ performance. Analysis of the EEG using the discrimination components learned at the peaks of the A/sub z/ fluctuations indicate 1) the presence of a positive evoked response, followed in time by a negative evoked response in strongly overlapping areas and 2) a component which is not correlated with the discriminator learned during the time-localized fluctuation. Results suggest that multiple signatures, varying over time, may exist for discriminating between target and distractor trials.

Hierarchical, multi-resolution models for object recognition: applications to mammographic computer-aided diagnosis

A fundamental problem in image analysis is the integration of information across scale to detect and classify objects. We have developed, within a machine learning framework, two classes of multiresolution models for integrating scale information for object detection and classification-a discriminative model called the hierarchical pyramid neural network and a generative model called a hierarchical image probability model. Using receiver operating characteristic analysis, we show that these models can significantly reduce the false positive rates for a well-established computer-aided diagnosis system.