Title :
Massively Parallel Neural Signal Processing on a Many-Core Platform
Author :
Chen, Dan ; Wang, Lizhe ; Ouyang, Gaoxiang ; Li, Xiaoli
Author_Institution :
China Univ. of Geosci., Wuhan, China
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.
Keywords :
Hilbert transforms; computational complexity; medical signal processing; multiprocessing systems; neural nets; Hilbert-Huang transform; algorithm complexity; ensemble empirical mode decomposition method; many core platform; massively parallel neural signal processing; Electroencephalography; Graphics processing unit; Instruction sets; Parallel processing; Signal analysis; Time series analysis; White noise; CUDA; EEG; GPGPU; Parallel processing; epilepsy; many-core platform; neural signal analysis;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCSE.2011.20
