Assaying neural activity of children during video game play in public spaces: a deep learning approach
Assaying neural activity of children during video game play in public spaces: a deep learning approach
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DOI：
10.1088/1741-2552/ab1876
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发表时间：
2019-05
影响因子：
4
通讯作者：
Akshay Sujatha Ravindran;Aryan Mobiny;Jesús G. Cruz-Garza;A. Paek;Anastasiya E. Kopteva;José L Contreras Vidal
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作者：
Akshay Sujatha Ravindran;Aryan Mobiny;Jesús G. Cruz-Garza;A. Paek;Anastasiya E. Kopteva;José L Contreras Vidal
Objective.了解发育中大脑的神经活动模式仍然是神经科学的重大挑战之一。发展中的神经网络可能被赋予与环境背景、年龄、性别和其他变量相关的功能上重要的可变性。因此，我们在一个刺激的博物馆环境中对典型发育中的儿童进行了实验，并测试了使用深度学习技术帮助识别与不同条件相关的大脑活动模式的可行性。Approach.在休斯顿儿童博物馆采集了儿童在休息和玩视频游戏（VGP）时的四通道干EEG移动的脑-体成像数据。使用基于卷积神经网络（CNN）的数据驱动方法来描述EEG中的潜在特征表示及其辨别任务和性别的能力。在休息条件下，作为年龄的函数的EEG的频谱特征的变异性进行了分析。主要结果。α功率（7-13 Hz）在休息期间较高，而θ功率（4-7 Hz）在VGP期间较高。当区分男性和女性时，β（13-18 Hz）功率是最显著的特征，女性更高。使用来自两个颞顶通道的数据对VGP和静息状态进行分类，获得了67%的留一受试者交叉验证准确率。睡眠相关的变化，在休息时的EEG频谱内容与以前的发展研究在实验室环境中进行的年龄和EEG功率之间的反比关系是一致的。意义这些发现是第一个使用深度学习框架获取，量化和解释在VGP期间观察到的大脑模式，并在博物馆环境中自由行为的儿童中休息。该研究展示了深度学习如何作为一种数据驱动的方法来识别数据中的模式，并探讨了在自然和引人入胜的环境中进行涉及儿童的实验的问题和潜力。
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.