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Independent modulators mediate spectra of multiple brain processes in a VR-based driving experiment

机译:独立的调制器在基于VR的驾驶实验中介导多个大脑过程的光谱

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摘要

This study explores the use of Independent Component Analysis (ICA) applied to normalized logarithmic spectral changes in the activities of brain processes separated by spatial filters learned from electroencephalogram (EEG) data using a temporal ICA. EEG data were collected during 1-2 hour virtual-reality based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced drifts using the steering wheel. ICA was first applied to 30-channel EEG data to separate the recorded signals into a sum of maximally temporally independent components (ICs) for each of 15 subjects. Logarithmic spectra of IC activities were then submitted to PCA-ICA to find spectrally fixed and temporally independent modulator (IM) processes. The second ICA detected and modeled independent co-modulatory systems that multiplicatively affect the activities of spatially distinct IC processes. Across subjects, we found two consistent temporally independent modulators: theta-beta and alpha modulators that mediate spectral activations of the distinct cortical areas when the participants experience waves of alternating alertness and drowsiness during long hour simulated driving. Furthermore, the time courses of the theta-beta modulator were highly correlated with concurrent changes in subject driving error (a behavioral index of drowsiness).
机译:这项研究探索了将独立成分分析(ICA)应用于脑过程活动的归一化对数光谱变化的过程,该过程由使用时域ICA从脑电图(EEG)数据中学习到的空间过滤器隔开。在基于虚拟现实的1-2小时驾驶实验中收集了EEG数据,指示受试者保持其巡航位置并使用方向盘补偿随机引起的漂移。 ICA首先应用于30通道EEG数据,以将记录的信号分离为15个受试者中每个受试者的最大时间独立分量(IC)之和。然后将IC活动的对数光谱提交给PCA-ICA,以查找光谱固定且时间独立的调制器(IM)过程。第二个ICA检测并建模了独立的协调制系统,该系统成倍地影响空间上不同的IC工艺的活动。在整个受试者中,我们发现了两个一致的时间独立的调制器:theta-beta和alpha调制器,当参与者在长时间的模拟驾驶中遇到警觉和嗜睡交替时,它们会介导不同皮层区域的光谱激活。此外,θ-β调制器的时程与受试者驾驶错误(睡意的行为指标)的同时变化高度相关。

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