EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention.
EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention.
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DOI：
10.1109/tbme.2017.2693157
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发表时间：
2018-01
期刊：
影响因子：
--
通讯作者：
Sarkar N
中科院分区：
文献类型：
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作者：
Fan J;Wade JW;Key AP;Warren ZE;Sarkar N
建立能够识别自闭症谱系障碍(ASD)个体在驾驶技能训练中的情绪状态和精神负荷的组级分类模型。20名患有自闭症的青少年参加了一项基于虚拟现实驾驶模拟器的六节课实验，在此期间，他们的脑电(EEG)数据被记录下来，同时记录下驾驶事件以及治疗师对他们的情感状态和精神负荷的评分。采用统计特征、分维特征、基于高阶交叉(HOC)的特征、频带功率特征和BIN(Δf=2 Hz)功率特征等5种特征生成方法提取相关特征。采用两步特征校准法消除个体差异。最后，对基于k-近邻算法和单变量特征选择方法的二值分类结果进行留一主题嵌套交叉验证，比较特征类型，识别区别性特征。最好的分类结果是使用垃圾箱中关于参与(0.95)和无聊(0.78)的能力特征，以及关于享受(0.90)、挫折(0.88)和工作负荷(0.86)的基于特定组织的特征。基于离线脑电信号的组级分类模型对于识别驾驶背景下自闭症患者的情感强度和工作负荷的低强度和高强度是可行的。然而，尽管这些模型在在线自适应驾驶任务中的适用性前景看好，但仍需进一步开发。所开发的模型为基于EEG的被动脑计算机接口系统提供了基础，该系统具有通过基于情感和工作负荷的个性化驾驶技能训练干预而使ASD患者受益的潜力。
To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.