Padilla-Buritica Jorge I, Ferrandez-Vicente Jose M, Castaño German A, Acosta-Medina Carlos D
Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia.
Diseño Electrónico y Técnicas de Tratamiento de Señales, Universidad Politécnica de Cartagena, Cartagena, Spain.
Front Neurosci. 2020 May 5;14:446. doi: 10.3389/fnins.2020.00446. eCollection 2020.
Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.
在假定异常刺激的差异贡献能够被准确评估的情况下，oddball任务的神经反应可被用作一种生理生物标志物来评估大脑的信息处理潜能。然而，神经活动的非平稳性导致大脑网络随时间大幅波动，从而恶化了成对协同作用的估计。为了应对神经反应的时间变异性，我们开发了一种逐段多主体分析方法，该方法在平稳性假设成立的一组时间间隔上应用。为了将整个刺激锁定时期分割成多个时间窗口，我们对脑电图记录的两种逐段分割方法进行了实验：一种是固定时间窗口，在该窗口下功能连接性（FC）测量的估计满足给定的置信水平；另一种是可变时间窗口，它在时变分类器性能的变化点处进行分割。使用加权锁相指数作为功能连接性指标，我们在真实世界的脑电图数据中进行了验证，证明了可变时间分割与监督阈值方法相结合时在连接性提取方面的有效性。因此，我们对脑电图数据进行了逐段组水平分析，该分析处理非平稳功能连接性测量，更仔细地评估连接节点集在区分标记的oddball反应中的贡献。
