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Analysis of neural signals (such as EEG) has long been a hot topic in neuroscience community due to neural signals' nonlinear and non-stationary features. Recent advances of experimental methods and neuroscience research have made neural signals constantly massive and analysis of these signals highly compute-intensive. Analysis of neural signals has been routinely performed upon CPU-based computer...
Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized...
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