Title

Parallel Processing Of Massive Eeg Data With Mapreduce

Abstract

Analysis of neural signals like electroencephalogram (EEG) is one of the key technologies in detecting and diagnosing various brain disorders. As neural signals are non-stationary and non-linear in nature, it is almost impossible to understand their true physical dynamics until the recent advent of the Ensemble Empirical Mode Decomposition (EEMD) algorithm. The neural signal processing with EEMD is highly compute-intensive due to the high complexity of the EEMD algorithm. It is also dataintensive because 1) EEG signals contain massive data sets 2) EEMD has to introduce a large number of trials in processing to ensure precision. The MapReduce programming mode is a promising parallel computing paradigm for data intensive computing. To increase the efficiency and performance of the neural signal analysis, this research develops parallel EEMD neural signal processing with MapReduce. In this paper, we implement the parallel EEMD with Hadoop in a modern cyberinfrastructure. Test results and performance evaluation show that parallel EEMD can significantly improve the performance of neural signal processing. © 2012 IEEE.

Publication Date

12-1-2012

Publication Title

Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS

Number of Pages

164-171

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICPADS.2012.32

Socpus ID

84874084750 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84874084750

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