Real or Virtual? Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios
This addresses the need for reliable brain-computer interfaces in augmented reality applications, though it is incremental as it builds on existing EEG classification methods.
The study tackled the problem of differentiating whether a user's attended target in augmented reality is real or virtual by using machine learning to classify EEG data, achieving over 70% accuracy in person-dependent classification and above-chance results for 6 out of 20 participants in person-independent classification.
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.