Exploration of Person-Independent BCIs for Internal and External Attention-Detection in Augmented Reality

Adding attention-awareness to an Augmented Reality setting by using a Brain-Computer Interface promises many interesting new applications and improved usability. The possibly complicated setup and relatively long training period of EEG-based BCIs however, reduce this positive effect immensely. In this study, we aim at finding solutions for person-independent, training-free BCI integration into AR to classify internally and externally directed attention. We assessed several different classifier settings on a dataset of 14 participants consisting of simultaneously recorded EEG and eye tracking data. For this, we compared the classification accuracies of a linear algorithm, a non-linear algorithm, and a neural net that were trained on a specifically generated feature set, as well as a shallow neural net for raw EEG data. With a real-time system in mind, we also tested different window lengths of the data aiming at the best payoff between short window length and high classification accuracy. Our results showed that the shallow neural net based on 4-second raw EEG data windows was best suited for real-time person-independent classification. The accuracy for the binary classification of internal and external attention periods reached up to 88% accuracy with a model that was trained on a set of selected participants. On average, the person-independent classification rate reached 60%. Overall, the high individual differences could be seen in the results. In the future, further datasets are necessary to compare these results before optimizing a real-time person-independent attention classifier for AR.