  Augmented Reality is the fusion of virtual components and our real
surroundings. The simultaneous visibility of generated and natural objects
often requires users to direct their selective attention to a specific target
that is either real or virtual. In this study, we investigated whether this
target is real or virtual by using machine learning techniques to classify
electroencephalographic (EEG) data collected in Augmented Reality scenarios. A
shallow convolutional neural net classified 3 second data windows from 20
participants in a person-dependent manner with an average accuracy above 70\%
if the testing data and training data came from different trials.
Person-independent classification was possible above chance level for 6 out of
20 participants. Thus, the reliability of such a Brain-Computer Interface is
high enough for it to be treated as a useful input mechanism for Augmented
Reality applications.
