Math NIC nac

• Parallel ICA Methods for EEG Neuroimaging By: Dan B. Keith, Christian C. Hoge, Robert M. Frank, and Allen D. Malony.

  Published at IPDPS conference 2006.

  Abstract: 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 Infomax) that exploit paral lelism 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 [4] is described, as well as future plans for this research.

• Framework for Evaluating ICA Methods of Artifact Removal from Multichannel EEG By: Kevin A. Glass, Gwen A. Frishkoff, Robert M. Frank, Colin Davey, Joseph Dien, Allen D. Malony, and Don M. Tucker.

  Abstract: We present a method for evaluating ICA separation of artifacts from EEG (electroencephalographic) data. Two algorithms, Infomax and FastICA, were applied to "synthetic data," created by superimposing simulated blinks on a blink-free EEG. To examine sensitivity to different data characteristics, multiple datasets were constructed by varying properties of the simulated blinks. ICA was used to decompose the data, and each source was cross-correlated with a blink template. Different thresholds for correlation were used to assess stability of the algorithms. When a match between the blink-template and the decomposition was obtained, the contribution of the source was subtracted from the EEG. Since the original data were known a priori to be blink-free, it was possible to compute the correlation between these "baseline" data and the results of different decompositions. By averaging the filtered data, time-locked to the simulated blinks, we illustrate effects of different outcomes for EEG waveform and topographic analysis.