Massively parallel non-stationary EEG data processing on GPGPU platforms with Morlet continuous wavelet transform

Abstract

Abstract Morlet continuous wavelet transform (MCWT) has been widely used to process non-stationary electro-encephalogram (EEG) data. Nowadays, the MCWT application for processing EEG data is time-sensitive and data-intensive due to quickly increasing problem domain sizes and advancing experimental techniques. In this paper, we proposed a massively parallel MCWT approach based on GPGPU to address this research challenge. The proposed approach treats MCWT as four main computing sub-procedures and parallelizes them with CUDA correspondingly. We focused on optimizing FFT on GPUs to improve the performance of MCWT. Extensive experiments have been carried out on Fermi and Kepler GPUs and a Fermi GPU cluster. The results indicate that (1) the proposed approach (especially on Kepler GPU) can ensure encouraging runtime performance of processing non-stationary EEG data in contrast to CPU-based MCWT, (2) the performance can further be improved on the GPU cluster but performance bottleneck exists when running multiple GPGPUs on one node, and (3) tuning an appropriate FFT radix is important to the performance of our MCWT.