标题： 使用信息几何表征脑电中的高阶交互作用 显示英文标题

标题： Using Information Geometry to Characterize Higher-Order Interactions in EEG

摘要： 在神经科学中，信息几何（IG）的方法已被成功应用于从尖峰序列数据中二进制向量的建模，利用Kullback-Leibler散度和互信息的正交分解来分离神经元之间的不同阶交互作用。 虽然尖峰序列数据可以用二进制模型很好地近似，但在此我们把这些IG方法应用于脑电图（EEG）的数据，这是一种连续信号，需要适当的离散化策略。 我们开发并比较了三种不同的二值化方法，并用它们来识别涉及想象运动任务中的三阶交互作用。 这些交互作用的统计显著性是通过相位随机化辅助数据来评估的，这种数据消除了高阶依赖关系，同时保留了原始信号的频谱特征。 我们通过在前向模型中实现已知的二阶和三阶依赖关系来验证我们的方法，并量化了分析不同步骤中的信息衰减。 这表明，信息损失最大的阶段是从理想化的二进制情况转变为使用振荡信号强制这些依赖关系时。 当应用于真实的EEG数据集时，我们的分析在任务条件下检测到了统计显著的三阶交互作用，尽管数据相对稀疏（每种条件45次试验）。 这项工作证明，当与适当的二值化方案结合时，IG方法可以成功地从连续的神经记录中提取真正的高阶依赖关系。 显示英文摘要

摘要： In neuroscience, methods from information geometry (IG) have been successfully applied in the modelling of binary vectors from spike train data, using the orthogonal decomposition of the Kullback-Leibler divergence and mutual information to isolate different orders of interaction between neurons. While spike train data is well-approximated with a binary model, here we apply these IG methods to data from electroencephalography (EEG), a continuous signal requiring appropriate discretization strategies. We developed and compared three different binarization methods and used them to identify third-order interactions in an experiment involving imagined motor movements. The statistical significance of these interactions was assessed using phase-randomized surrogate data that eliminated higher-order dependencies while preserving the spectral characteristics of the original signals. We validated our approach by implementing known second- and third-order dependencies in a forward model and quantified information attenuation at different steps of the analysis. This revealed that the greatest loss in information occurred when going from the idealized binary case to enforcing these dependencies using oscillatory signals. When applied to the real EEG dataset, our analysis detected statistically significant third-order interactions during the task condition despite the relatively sparse data (45 trials per condition). This work demonstrates that IG methods can successfully extract genuine higher-order dependencies from continuous neural recordings when paired with appropriate binarization schemes.