Though Electroencephalography (EEG)-based brain-computer interfaces (BCI) have come to outperform pure computer vision algorithms on difficult image triage tasks, none of these BCIs have leveraged the effects of motion on the human visual attention system. Here we consider the advantages of leveraging the effects of motion by testing a new method for EEG-based target detection using Rapid Serial Visual Presentation (RSVP) of short moving image clips. Comparatively, canonical methods present the operator with still images only. Our experiments show that presenting moving images in RSVP instead of still images significantly increases performance (p < 0.0005). In addition, we investigate the effect of increasing target load in RSVP of moving image clips and find some support for the hypothesis that detection will decrease as target load is increased.

We develop a Bayesian sequential model for category learning. The sequential model updates two category parameters, the mean and the variance, over time. We define conjugate temporal priors to enable closed form solutions to be obtained. This model can be easily extended to supervised and unsupervised learning involving multiple categories. To model the spacing effect, we introduce a generic prior in the temporal updating stage to capture a learning preference, namely, less change for repetition and more change for variation. Finally, we show how this approach can be generalized to efficiently performmodel selection to decide whether observations are from one or multiple categories.