A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI

• Published in: Computational and mathematical methods in medicine Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Gu, Yuhong, Wang, Wenfeng, Zhang, Xinman, Xiong, Qi
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi
• Subjects: Algorithms; Big Data; Brain - physiology; Brain-Computer Interfaces - statistics & numerical data; Databases, Factual - statistics & numerical data; Electroencephalography - instrumentation; Electroencephalography - statistics & numerical data; Fourier Analysis; Humans; Pattern Recognition, Automated - statistics & numerical data; Programming Languages; Signal Processing, Computer-Assisted
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2020/9812019

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.