Fan Jing, Wade Joshua W, Bian Dayi, Key Alexandra P, Warren Zachary E, Mion Lorraine C, Sarkar Nilanjan
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:3767-70. doi: 10.1109/EMBC.2015.7319213.
Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental disorder. Individuals with ASD often have deficits in social communication skills as well as adaptive behavior skills related to daily activities. We have recently designed a novel virtual reality (VR) based driving simulator for driving skill training for individuals with ASD. In this paper, we explored the feasibility of detecting engagement level, emotional states, and mental workload during VR-based driving using EEG as a first step towards a potential EEG-based Brain Computer Interface (BCI) for assisting autism intervention. We used spectral features of EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings of behavioral engagement, enjoyment, frustration, boredom, and difficulty to train a group of classification models. Seven classification methods were applied and compared including Bayes network, naïve Bayes, Support Vector Machine (SVM), multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states. Such results may lead to an adaptive closed-loop VR-based skill training system for use in autism intervention.
自闭症谱系障碍（ASD）是一种普遍且代价高昂的神经发育障碍。患有ASD的个体通常在社交沟通技能以及与日常活动相关的适应性行为技能方面存在缺陷。我们最近设计了一种新颖的基于虚拟现实（VR）的驾驶模拟器，用于对患有ASD的个体进行驾驶技能训练。在本文中，我们探索了使用脑电图（EEG）检测基于VR驾驶过程中的参与度水平、情绪状态和心理负荷的可行性，这是迈向潜在的基于EEG的脑机接口（BCI）以辅助自闭症干预的第一步。我们使用来自14通道EEG神经头戴式设备的EEG信号的频谱特征，以及治疗师对行为参与度、愉悦感、挫败感、无聊感和难度的评分，来训练一组分类模型。应用并比较了七种分类方法，包括贝叶斯网络、朴素贝叶斯、支持向量机（SVM）、多层感知器、K近邻（KNN）、随机森林和J48。分类结果很有前景，在对参与度和心理负荷进行分类时准确率超过80%，在对情绪状态进行分类时准确率超过75%。这些结果可能会促成一个用于自闭症干预的基于VR的自适应闭环技能训练系统。
