Massively Parallel Neural Signal Processing on a Many-Core Platform

Authors:
Dan Chen;Lizhe Wang;Gaoxiang Ouyang;Xiaoli Li
Affiliations:
China University of Geosciences;Indiana University;Yanshan University, Qinhuangdao, China;Yanshan University, Qinhuangdao, China
Venue:
Computing in Science and Engineering
Year:
2011

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Abstract

Although the ensemble empirical mode decomposition (EEMD) method and Hilbert-Huang transform (HHT) offer an unrivaled opportunity to understand neural signals, the EEMD algorithm's complexity and neural signals' massive size have hampered EEMD application. However, a new approach using a many-core platform has proven both efficient and effective for massively parallel neural signal processing.