Abstract
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.
Highlights
EEG is a recorded signal of electrical activity of the brain, which is collected from the scalp through electrodes. e application of EEG has important practical value in medical treatment, military, sports, and the intelligence fields, which has been widely recognized by all the researchers
We present a parallel framework for the large dataset to extract power spectrum features of EEG signals, which can be implemented in Linux and MPI environment
E main contributions of this paper are threefold: (1) According to the principle of Welch algorithm, we propose a parallel framework of Welch algorithm, PFwelch, to compute power spectral density (PSD) of EEG. e architecture of PFwelch is based on the master-slave mode. e EEG signals recorded by each channel are divided into N overlapping data segments. en, the N segments are computed by the master and slave node in parallel. e results are collected and summarized by the master node. e final Welch PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel
Summary
EEG is a recorded signal of electrical activity of the brain, which is collected from the scalp through electrodes. e application of EEG has important practical value in medical treatment, military, sports, and the intelligence fields, which has been widely recognized by all the researchers. EEG is a recorded signal of electrical activity of the brain, which is collected from the scalp through electrodes. Ese signals correspond to specific brain activity. E system is connected to the computer for real-time dialling by interpreting the thinking mode of the brain as corresponding numbers [1,2,3,4,5,6]. E increase in the number of electrodes enables recording of huge data thereby making the data processing stage in Figure 1 more important and complicated. EEG devices with as many as 256 electrodes have been used, as shown in Figure 2(a). e increase in the number of electrodes enables recording of huge data thereby making the data processing stage in Figure 1 more important and complicated. is consumes a lot of computer resources and leads to poor data extraction thereby directly affecting the accuracy of classification. ere are many signal processing methods which can be used to extract EEG features with good discrimination. ese methods include time domain analysis, Computational and Mathematical Methods in Medicine
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