Deep Learning Approaches for P300 Classification in Image Triage: Applications to the NAILS Task

This paper describes the rationale behind and results of five evaluation submissions to the NAILS (Neurally Augmented Image Labelling Strategies) challenge at the NTCIR-13 conference. Image triage is a timeand resourceintensive process for human labelers. Researchers have identified a potential P300-based BCI solution to alleviate the strain of manual labeling. The NAILS dataset was designed to capture the P300 signal over various image search activities and to act as a benchmark dataset for P300 detection methods. Here we describe approaches that utilize crossand within-subject training using our in-house Convolutional Neural Network (CNN) EEGNet, and another state-of-the art eventrelated-potential approach based on xDAWN spatial filtering combined with Information Geometry using Riemannian manifolds. We show improved performance with within-subject training, more data, and modifications to the EEGNet model, and briefly discuss the implications of using certain training data over others.