# Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals

arXiv: https://arxiv.org/abs/1902.01799 · HTML: https://mlsanity.com/paper/1902.01799/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [EEG-based Classification of Drivers Attention using Convolutional Neural Network](https://mlsanity.com/paper/2108.10062/) (2021)

## Abstract (arXiv)

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.