EEG-Based Classification of Internally- and Externally-Directed Attention in an Augmented Reality Paradigm

One problem faced in the design of Augmented Reality (AR) applications is the interference of virtually displayed objects in the user’s visual field, with the current attentional focus of the user. Newly generated content can disrupt internal thought processes. If we can detect such internally-direc...

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Bibliographic Details
Published in:Frontiers in human neuroscience 2019-10, Vol.13, p.348-348
Main Authors: Vortmann, Lisa-Marie, Kroll, Felix, Putze, Felix
Format: Article
Language:English
Subjects:
Augmented reality
Brain research
brain-computer interface
Classification
Discriminant analysis
EEG
Electroencephalography
external attention
Feasibility studies
Human Neuroscience
internal attention
Learning algorithms
Visual field
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Summary:One problem faced in the design of Augmented Reality (AR) applications is the interference of virtually displayed objects in the user’s visual field, with the current attentional focus of the user. Newly generated content can disrupt internal thought processes. If we can detect such internally-directed attention periods, the interruption could either be avoided or even used intentionally. In this work, we designed a spacial alignment task in AR with two conditions: one with externally-directed attention and one with internally-directed attention. Apart from the direction of attention, the two tasks were identical. During the experiment, we performed a 16-channel EEG recording, which was then used for a binary classification task. Based on selected band power features, we trained a Linear Discriminant Analysis classifier to predict the label for a 13-second window of each trial. Parameter and feature selection, as well as the training of the classifier, were done in a person-dependent manner. The scores were calculated in a 5-fold cross-validation. We achived an average best score of approximately 84.68% accuracy (±10.6%, range = [63.8%, 98.6%], 6 participants>90%). Our results show that it is possible to discriminate the two states with simple machine learning mechanisms. The analysis of additionally collected data dispels any doubt that we classified anything but the actual difference in the attentional focus. We conclude that a real-time assessment of internal and external attention in an AR setting will be possible.
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2019.00348