TopographyNET

Abstract

We often find our minds drifting off a current task towards something else, a phenomenon known as mind wandering. Mind wandering can negatively impact performance of many tasks (e.g., learning). Thus, it is crucial to find a way to detect mind wandering. Using deep learning and electroencephalogram (EEG) seems very promising. EEG systems offer high temporal precision and accessibility, and deep learning can automatically extract features from EEG signals. However, three key challenges hinder deep learning performance: the dynamic and distributed nature of mind wandering, small EEG datasets, and diverse EEG systems. Existing deep learning solutions do not perform well on the small datasets and cannot use data from other EEG systems. We propose a novel deep learning model, TopographyNET, which 1) captures the dynamic and distributed properties through spatial and temporal processing via 2D topographic scalp maps and a recurrent neural network; 2) applies transfer learning to address the issue of small datasets using a pretrained image classification neural network on topographic scalp maps; and 3) represents data in a uniform format and thus enables the usage of EEG data from diverse systems. Compared to an existing solution, our approach achieves a much higher classification accuracy. In addition, we present the hyperparameter tuning process that helped us achieve a high classification accuracy.