Abstract
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) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG 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. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG 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.
Highlights
Information 2021, 12, 226. https://One of the many challenges that our brain is faced with daily is the filtering and processing of vast amounts of information about our surroundings
In [54], we showed that it was possible to classify internal and external user attention in an augmented reality paradigm and in [55], a first real-time attention-aware smart-home system in Augmented reality (AR) was implemented and tested
The average classification results of the 10 runs that were performed per person are all significantly better than chance
Summary
One of the many challenges that our brain is faced with daily is the filtering and processing of vast amounts of information about our surroundings. The input recorded by our auditory, visual, olfactory, gustatory, proprioceptive, and tactile senses is immense at almost any given moment. To survive in a world of sensory overload, we need to give meaning to this available information and focus on the most important aspects of the input. The cognitive process of directing this focus on a selected sensation is the core of attention mechanisms [1]. Subtle differences can still be found between different definitions of “attention” because many processes are still under examination. The meaning, assumptions, and implications about the importance of consciousness, concentration, willingness, allocation of resources, memory, and vigilance are yet to be understood
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
