Assaying neural activity of children during video game play in public spaces: a deep learning approach

• Published in: Journal of neural engineering Vol. 16; no. 3; pp. 36028 - 36039
• Main Authors: Sujatha Ravindran, Akshay, Mobiny, Aryan, Cruz-Garza, Jesus G, Paek, Andrew, Kopteva, Anastasiya, Contreras Vidal, José L
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.06.2019
• Subjects: brain pattern; children; CNN; deep learning; EEG; video game
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ab1876

Objective. Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environmental context, age, gender, and other variables. Therefore, we conducted experiments with typically developing children in a stimulating museum setting and tested the feasibility of using deep learning techniques to help identify patterns of brain activity associated with different conditions. Approach. A four-channel dry EEG-based Mobile brain-body imaging data of children at rest and during videogame play (VGP) was acquired at the Children's Museum of Houston. A data-driven approach based on convolutional neural networks (CNN) was used to describe underlying feature representations in the EEG and their ability to discern task and gender. The variability of the spectral features of EEG during the rest condition as a function of age was also analyzed. Main results. Alpha power (7-13 Hz) was higher during rest whereas theta power (4-7 Hz) was higher during VGP. Beta (13-18 Hz) power was the most significant feature, higher in females, when differentiating between males and females. Using data from both temporoparietal channels to classify between VGP and rest condition, leave-one-subject-out cross-validation accuracy of 67% was obtained. Age-related changes in EEG spectral content during rest were consistent with previous developmental studies conducted in laboratory settings showing an inverse relationship between age and EEG power. Significance. These findings are the first to acquire, quantify and explain brain patterns observed during VGP and rest in freely behaving children in a museum setting using a deep learning framework. The study shows how deep learning can be used as a data driven approach to identify patterns in the data and explores the issues and the potential of conducting experiments involving children in a natural and engaging environment.