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Neural Signals Evoked by Stimuli of Increasing Social Scene Complexity Are Detectable at the Single-Trial Level and Right Lateralized

机译:社交场景复杂性刺激引起的神经信号可在单次试验水平上检测到并进行右偏

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摘要

Classification of neural signals at the single-trial level and the study of their relevance in affective and cognitive neuroscience are still in their infancy. Here we investigated the neurophysiological correlates of conditions of increasing social scene complexity using 3D human models as targets of attention, which may also be important in autism research. Challenging single-trial statistical classification of EEG neural signals was attempted for detection of oddball stimuli with increasing social scene complexity. Stimuli had an oddball structure and were as follows: 1) flashed schematic eyes, 2) simple 3D faces flashed between averted and non-averted gaze (only eye position changing), 3) simple 3D faces flashed between averted and non-averted gaze (head and eye position changing), 4) animated avatar alternated its gaze direction to the left and to the right (head and eye position), 5) environment with 4 animated avatars all of which change gaze and one of which is the target of attention. We found a late (> 300 ms) neurophysiological oddball correlate for all conditions irrespective of their complexity as assessed by repeated measures ANOVA. We attempted single-trial detection of this signal with automatic classifiers and obtained a significant balanced accuracy classification of around 79%, which is noteworthy given the amount of scene complexity. Lateralization analysis showed a specific right lateralization only for more complex realistic social scenes. In sum, complex ecological animations with social content elicit neurophysiological events which can be characterized even at the single-trial level. These signals are right lateralized. These finding paves the way for neuroscientific studies in affective neuroscience based on complex social scenes, and given the detectability at the single trial level this suggests the feasibility of brain computer interfaces that can be applied to social cognition disorders such as autism.
机译:单次试验中神经信号的分类及其在情感和认知神经科学中的相关性研究仍处于起步阶段。在这里,我们使用3D人体模型作为关注目标,研究了社交场景复杂性增加的条件下的神经生理相关性,这在自闭症研究中也可能很重要。尝试对具有挑战性的EEG神经信号进行统计分类,以检测随着社交场景复杂性增加的奇异球刺激。刺激具有奇数球结构,如下所示:1)闪烁示意性眼睛,2)简单3D面孔在平视和非平均视线之间闪烁(仅眼睛位置发生变化),3)简单3D面孔在平视和非平均视线之间闪烁(更改头部和眼睛的位置),4)动画化身的注视方向左右交替(头部和眼睛位置),5)具有4个动画化身的环境,所有这些动画化身都改变了注视,其中一个是关注的目标。我们发现,对于所有情况而言,晚期(> 300毫秒)神经生理学奇异球相关性,无论其复杂性如何,均通过重复测量ANOVA进行评估。我们尝试使用自动分类器对该信号进行单次试验检测,并获得了约79%的显着平衡精度分类,考虑到场景复杂性,这一点值得注意。横向化分析显示了仅针对更复杂的现实社会场景的特定右横向化。总而言之,具有社交内容的复杂生态动画会引发神经生理事件,甚至在单次审判中也可以表征。这些信号右偏。这些发现为基于复杂社交场景的情感神经科学研究中的神经科学研究铺平了道路,并且鉴于在单个试验级别上具有可检测性,这表明可将脑计算机接口应用于自闭症等社交认知障碍的可行性。

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