Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic
oscillatory components (DMs). DMs can improve the accuracy of neural decoding
when used with the nonlinear Grassmann kernel, compared to conventional power
features. However, such kernel-based machine learning algorithms have three
limitations: large computational time preventing real-time application,
incompatibility with non-kernel algorithms, and low interpretability. Here, we
propose a mapping function corresponding to the Grassmann kernel that
explicitly transforms DMs into spatial DM (sDM) features, which can be used in
any machine learning algorithm. Using electrocorticographic signals recorded
during various movement and visual perception tasks, the sDM features were
shown to improve the decoding accuracy and computational time compared to
conventional methods. Furthermore, the components of the sDM features
informative for decoding showed similar characteristics to the high-$\gamma$
power of the signals, but with higher trial-to-trial reproducibility. The
proposed sDM features enable fast, accurate, and interpretable neural decoding.

我们提出了一种将动态模式分解显式地转化为空间动态模式特征的映射函数，这些特征可以应用于任何机器学习算法，用于提高神经解码的准确性和计算时间，并具有更高的可解读性。

电 Ccortico 动态模态分解的快速准确可解释解码

Fast, accurate, and interpretable decoding of electrocorticographic  signals using dynamic mode decomposition

In this paper, we developed a model-based and a data-driven estimator for
directed information (DI) to infer the causal connectivity graph between
electrocorticographic (ECoG) signals recorded from brain and to identify the
seizure onset zone (SOZ) in epileptic patients. Directed information, an
information theoretic quantity, is a general metric to infer causal
connectivity between time-series and is not restricted to a particular class of
models unlike the popular metrics based on Granger causality or transfer
entropy. The proposed estimators are shown to be almost surely convergent.
Causal connectivity between ECoG electrodes in five epileptic patients is
inferred using the proposed DI estimators, after validating their performance
on simulated data. We then proposed a model-based and a data-driven SOZ
identification algorithm to identify SOZ from the causal connectivity inferred
using model-based and data-driven DI estimators respectively. The data-driven
SOZ identification outperforms the model-based SOZ identification algorithm
when benchmarked against visual analysis by neurologist, the current clinical
gold standard. The causal connectivity analysis presented here is the first
step towards developing novel non-surgical treatments for epilepsy.

研究开发了基于模型和基于数据的估计器，用于直接推断脑电层析图（ECoG）信号之间的因果连通性图，并在癫痫患者中识别癫痫发作区（SOZ）。通过验证其在模拟数据上的表现，利用所提出的 DI 估计器推断了五名癫痫患者的 ECoG 电极之间的因果连通性，并提出了一种基于模型和基于数据的 SOZ 识别算法来识别 SOZ，其中基于数据的 SOZ 识别优于基于模型的 SOZ 识别算法。