In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master-slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then, the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. The final PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85 GB EEG dataset, seven times faster than using Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract the EEG power spectrum.

中文翻译：

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MPI上脑电信号特征提取的并行算法框架。

在本文中，我们为大型数据集提出了一个基于MPI的并行框架，以提取脑电信号的功率谱特征，从而提高脑信号处理的速度。目前，韦尔奇方法已被广泛用于估计功率谱。但是，传统的Welch方法要花费大量时间，尤其是对于大型数据集。有鉴于此，我们将MPI添加到传统的Welch方法中，并将其开发为可重用的主从并行框架。只要将任何格式的EEG数据转换为指定格式的文本文件，该并行框架都可以快速提取功率谱特征。在提出的并行框架中，将由通道记录的EEG信号划分为N个重叠的数据段。然后，N的PSD段是由某些节点并行计算的。结果由主节点收集和汇总。每个通道的最终PSD结果保存在文本文件中，Microsoft Excel可以读取和分析该文件。该框架不仅可以在群集上实现，而且可以在台式计算机上实现。在实验中，我们将此框架部署在具有4核Intel CPU的台式计算机上。从2.85 GB EEG数据集中提取功率谱特征仅需几分钟，比使用Python快7倍。该框架使没有任何并行编程经验的用户可以轻松构建并行算法以提取EEG功率谱。