基于虚拟开车环境的自闭症儿童脑电样本熵

自闭症谱系障碍是一种涉及感觉、情感、记忆、语言、智力、动作等认知功能和执行功能障碍的精神疾病. 本文从神经工效学角度出发, 用虚拟开车环境作为复杂多任务激励源将大脑系统与人体动作控制等有机地结合起来, 通过对脑电信号的滑动平均样本熵分析来探索自闭症儿童在虚拟开车环境中的脑活动特征. 研究发现不论是休息状态还是开车状态, 自闭症患者的滑动平均样本熵总体上低于健康者, 尤其在前额叶、颞叶、顶叶和枕叶功能区, 表明自闭症儿童的行为适应性较低. 不过, 自闭症患者的开车状态与健康受试者的休息状态比较接近, 表明虚拟开车环境或许有助于自闭症患者的干预治疗. 此外, 自闭症患者在颞叶区呈现显著性右半球优势性. 本研究为进一步深入开展自闭症疾病的机理研究及其诊断、评估和干预等研究提供一种新的研究思路.

Sample entropy of electroencephalogram for children with autism based on virtual driving game

Autism spectrum disorder is a kind of mental disease which involves the disorders of the perception, emotion, memory, language, intelligence, thinking, action, etc. The aim of this paper is to investigate the brain activity characteristics of the children with autism during complex environments by analyzing electroencephalogram (EEG) signals from the neuroergonomics perspective. The virtual driving environment as a complex multi-task source is used to organically connect brain systems with human motion control. The 14-channel EEG signals are obtained including the EEG baseline signals on a resting state (about 3 min) and the EEG activity signals during driving (about 5 min). The method of the shift average sample entropy is proposed to deal with EEG signals in the resting and the virtual driving environments. Considering the highly complex hyper-dimensional characteristics of EEG signals, the different embedding dimensions (such as 2 and 6 dimensions) are analyzed in the sample entropy estimation. The results show that the average sample entropy values of autism spectrum disorder (ASD) subjects are lower than those of healthy subjects during resting and driving, respectively, especially in the prefrontal lobe, temporal lobe, parietal lobe and occipital lobe during resting and in temporal lobe and occipital lobe during driving. It indicates that ASD children lack the ability to adapt easily their behaviors. Meanwhile, like healthy subjects, the average sample entropy values of ASD subjects during driving are higher than those during resting as a whole. Moreover, the EEG activity signals of ASD are obviously higher than the EEG baseline signals in prefrontal lobe, frontal lobe, frontal central lobe and temporal lobe regions in 95% significant level. And for healthy subjects, the activity signals are significantly higher than the baseline signals only in parietal lobe region. Furthermore, the brain activities of ASD subjects during driving come closer to those of healthy subjects during resting. It suggests that the virtual driving environment may be helpful for the treatment of ASD individuals. In addition, the ASD and healthy subjects have a certain right hemisphere dominance in the whole region except in the parietal lobe region. In the parietal lobe region, they have some left hemisphere dominance, especially during driving. And for ASD subjects, there is the significant right hemisphere dominance in the temporal lobe in 95% confidence level no matter whether in the resting state or in the driving state. The results show that it is suitable for the shift average sample entropy analysis to study the brain activities of ASD individuals. This study will provide a new research method for the further research on the mechanism of autism and its diagnosis, evaluation and intervention.