  Mind wandering (MW) is a ubiquitous phenomenon which reflects a shift in
attention from task-related to task-unrelated thoughts. There is a need for
intelligent interfaces that can reorient attention when MW is detected due to
its detrimental effects on performance and productivity. In this paper, we
propose a deep learning model for MW detection using Electroencephalogram (EEG)
signals. Specifically, we develop a channel-wise deep convolutional neural
network (CNN) model to classify the features of focusing state and MW extracted
from EEG signals. This is the first study that employs CNN to automatically
detect MW using only EEG data. The experimental results on the collected
dataset demonstrate promising performance with 91.78% accuracy, 92.84%
sensitivity, and 90.73% specificity.
