AUTHOR=Vortmann Lisa-Marie , Kroll Felix , Putze Felix 

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

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2019.00348

DOI=10.3389/fnhum.2019.00348

ISSN=1662-5161

ABSTRACT=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.