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首页> 外文期刊>Journal of neural engineering >Assaying neural activity of children during video game play in public spaces: a deep learning approach
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Assaying neural activity of children during video game play in public spaces: a deep learning approach

机译：在公共场所进行视频游戏时分析儿童的神经活动：一种深度学习方法

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摘要

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.
机译：目的。了解发育中的大脑中的神经活动模式仍然是神经科学面临的重大挑战之一。不断发展的神经网络可能具有与环境，年龄，性别和其他变量相关的功能上重要的可变性。因此，我们在一个充满刺激的博物馆环境中对通常发育中的儿童进行了实验，并测试了使用深度学习技术来帮助识别与不同情况相关的大脑活动模式的可行性。方法。在休斯顿儿童博物馆获得了基于四通道干式EEG的静止和视频游戏中儿童的移动脑成像数据。基于卷积神经网络（CNN）的数据驱动方法用于描述EEG中的基本特征表示及其识别任务和性别的能力。还分析了静息状态下脑电波频谱特征随年龄的变化。主要结果。在休息期间，α功率（7-13 Hz）较高，而在VGP期间，θ功率（4-7 Hz）较高。贝塔（13-18 Hz）功率是最显着的特征，当区分男性和女性时，女性更高。使用来自两个颞叶通道的数据在VGP和休息状态之间进行分类，获得了67％的留一法则交叉验证的准确性。休息期间脑电波频谱含量与年龄有关的变化与以前在实验室环境中进行的发育研究一致，表明年龄与脑电功率之间呈反比关系。意义。这些发现是第一个获得，量化和解释在VGP期间观察到的大脑模式以及在博物馆环境中使用深度学习框架在行为自由的孩子中休息的大脑的方法。这项研究表明，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，并探索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。

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  • 来源
    《Journal of neural engineering》 |2019年第3期|036028.1-036028.12|共12页
  • 作者

    Ravindran Akshay Sujatha; Mobiny Aryan; Cruz-Garza Jesus G.; Paek Andrew; Kopteva Anastasiya; Vidal Jose L. Contreras;

  • 作者单位

    Univ Houston, Dept Elect & Comp Engn, Noninvas Brain Machine Interface Syst Lab, Houston, TX 77004 USA;

    Univ Houston, Dept Elect & Comp Engn, Noninvas Brain Machine Interface Syst Lab, Houston, TX 77004 USA;

    Univ Houston, Dept Elect & Comp Engn, Noninvas Brain Machine Interface Syst Lab, Houston, TX 77004 USA;

    Univ Houston, Dept Elect & Comp Engn, Noninvas Brain Machine Interface Syst Lab, Houston, TX 77004 USA;

    Univ Houston, Dept Elect & Comp Engn, Noninvas Brain Machine Interface Syst Lab, Houston, TX 77004 USA;

    Univ Houston, Dept Elect & Comp Engn, Noninvas Brain Machine Interface Syst Lab, Houston, TX 77004 USA;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    EEG; deep learning; CNN; video game; children; brain pattern;

    机译：脑电图;深度学习;CNN;视频游戏;儿童;脑模式;

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