Matlab is a popular interactive computing environment that gives scientists a powerful and easy to use set of analysis and visualization tools. However, Matlab has been criticized for performance limitations on computationally and memory intensive applications. In addition, because of Matlab's single-threaded processing model, long-running processes prevent a user from performing other tasks while computation occurs. We discuss a framework for remote, concurrent execution of tasks in the Matlab environment that address these shortcomings by asynchronously distributing tasks to dedicated high-performance compute servers. We employ a client-server model based on, web-standards and Matlab's built in scripting and Java environment.

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.

HiPerSAT, a C++ library and associated tools, processes large EEG data sets with statistical data whitening and ICA (Independent Component Analysis) methods. The library uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits available parallel hardware. ICA is a class of methods for analyzing a large set of data samples and deducing the independent components responsible for the observed data. ICA is used in EEG signal analysis to understand neurological components of dynamic brain activity. We present two ICA implementations (FastICA and Infomax) 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. In addition, the integration of this functionality into the MATLAB-based EEGLAB tools [6] is described, as well as future plans for this research.