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Augmented Reality is the fusion of virtual components and our realsurroundings. The simultaneous visibility of generated and natural objectsoften requires users to direct their selective attention to a specific targetthat is either real or virtual. In this study, we investigated whether thistarget is real or virtual by using machine learning techniques to classifyelectroencephalographic (EEG) data collected in Augmented Reality scenarios. Ashallow convolutional neural net classified 3 second data windows from 20participants 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 of20 participants. Thus, the reliability of such a Brain-Computer Interface ishigh enough for it to be treated as a useful input mechanism for AugmentedReality applications.

Real or Virtual? Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios