Discriminating object from non-object perception in a visual search task by joint analysis of neural and eyetracking data

Abstract

The single-trial classification of neural responses to stimuli is an essential element of non-invasive brain-machine interfaces (BMI) based on the electroencephalogram (EEG). However, typically, these stimuli are artificial and the classified neural responses only indirectly related to the content of the stimulus. Fixation-related potentials (FRP) promise to overcome these limitations by directly reflecting the content of visual information that is perceived. We present a novel approach for discriminating between single-trial FRP related to fixations on objects versus on a plain background. The approach is based on a source power decomposition that exploits fixation parameters as target variables to guide the optimization. Our results show that this method is able to classify object versus non-object epochs with a much better accuracy than reported previously. Hence, we provide a further step to exploiting FRP for more versatile and natural BMI.