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首页> 美国卫生研究院文献>Frontiers in Human Neuroscience >Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks
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Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

机译：在脑电微状态中的不确定性建模：使用概率聚类驱动的概率神经网络训练对真实和想象的运动进行分析

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Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.
机译：脑电图微状态估计过程的一部分涉及将脑电图通道数据聚集在全局场功率（GFP）最大值处，这很常见，是使用改进的K均值方法进行的。尽管在微状态分析的多个阶段都存在不确定性，包括GFP峰定义，聚类本身以及将微状态的聚类后分配回到感兴趣的EEG时间过程，但也已经确定性地进行了聚类。我们执行完全概率的微状态聚类和标记，以使用与KM最接近的概率模拟（称为模糊C均值（FCM））解决这些不确定性源。我们使用KM和FCM推断的簇分配作为目标标签来训练softmax多层感知器（MLP），然后允许对完整EEG数据进行概率标记，而不是通常使用的基于相关性的确定性微状态标签分配。我们评估了概率分析与确定性方法在记录的EEG数据中的优点，而参与者在109个受试者的公开数据集中执行了真实或想象的运动运动时，就进行了记录。尽管发现FCM组模板图在地形上几乎与KM相同，但后续的微状态标签分配仍存在很大不确定性。通常，在逐个时间点的基础上，想象的运动很难预测，与真实的运动相比，可能反映了想象中的大脑状态具有更大的探索性。我们发现，使用FCM而非使用KM可能会更明显一些关系，并建议未来的微状态分析应优选概率性而非确定性地执行，尤其是在诸如大脑计算机接口这样的情况下，其中微状态的训练和应用都需要考虑对于不确定性。概率神经网络驱动的微状态分配具有我们已经讨论的许多优点，这些优点可能会在未来的研究中进一步发展和利用。总之，概率聚类和概率神经网络驱动的微状态分析方法可能会更好地建模和揭示细节，并揭示当前确定性和二值化微状态分配和分析中隐藏的细节和可变性。

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