Parallel ICA methods for EEG neuroimaging

HiPerSAT, a C++ library and tools, processes EEG data sets with ICA (independent component analysis) methods. HiPerSAT uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits parallel hardware. ICA is a class of methods for analyzing a large set of data samples and extracting independent components that explain the observed data. ICA is used in EEG research for data cleaning and separation of spatiotemporal patterns that may reflect different underlying neural processes. We present two ICA implementations (FastICA and Info-max) that exploit parallelism to provide an EEG component decomposition solution of higher performance and data capacity than current MATLAB-based implementations. Experimental results and the methodology used to obtain them are presented. Integrating HiPerSAT with EEGLAB (A. Delorme and S. Makeig, 2004) is described, as well as future plans for this research.