Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals
This addresses the need for intelligent interfaces to reorient attention during MW, which can impair performance and productivity, but it is incremental as it applies an existing method (CNN) to a new application area.
The paper tackled the problem of automatically detecting mind wandering (MW) from EEG signals using a deep convolutional neural network (CNN), achieving 91.78% accuracy, 92.84% sensitivity, and 90.73% specificity.
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.