Background

The dynamic time warping (DTW) has recently been introduced to analyze neural signals such as EEG and fMRI where phase variability plays an important role in the data.

New method

In this study, we propose to adopt a more powerful method, referred to as the Fisher-Rao Registration (FRR), to study the phase variability.

Comparison with existing methods

We systematically compare FRR with DTW in three aspects: (1) basic framework, (2) mathematical properties, and (3) computational efficiency.

Results

We show that FRR has superior performance in all these aspects and the advantages are well illustrated with simulation examples.

Conclusions

We then apply the FRR method to two real experimental recordings – one fMRI and one EEG data set. It is found the FRR method properly removes the phase variability in each set. Finally, we use the FRR framework to examine brain networks in these two data sets and the result demonstrates the effectiveness of the new method.

中文翻译：

---

使用Fisher-Rao配准对具有相位变化的神经信号进行建模和分析。

背景

动态时间规整（DTW）最近已被引入来分析诸如EEG和fMRI等神经信号，其中相位可变性在数据中起着重要作用。

新方法

在这项研究中，我们建议采用一种更强大的方法，称为Fisher-Rao配准（FRR），以研究相位可变性。

与现有方法的比较

我们在三个方面系统地比较了FRR和DTW：（1）基本框架，（2）数学属性和（3）计算效率。

结果

我们表明，FRR在所有这些方面均具有优越的性能，并且通过仿真示例可以很好地说明其优势。

结论

然后，我们将FRR方法应用于两个真实的实验记录–一个fMRI和一个EEG数据集。发现FRR方法可以正确消除每组中的相位变化。最后，我们使用FRR框架检查了这两个数据集中的大脑网络，结果证明了该新方法的有效性。