Optimization Techniques for Independent Component Analysis with Applications to EEG Data

Georgiev, Pando; Cichocki, Andrzej; Bakardjian, Hovagim

doi:10.1007/978-1-4613-0225-4_3

Cited by 3 publications

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“…Both of these measures leave a lot to be desired, because cognitive commands to subjects may introduce additional complexity, while at the same time very slow eye movements are difficult to identify only by voltage thresholding because their amplitudes may be comparable to those of the underlying electroencephalogram. Recent studies have proposed artifact removal procedures based on estimation of correction coefficients [8] and independent component analysis [13,19,14,18,20], etc. The goal of the present section is to demonstrate that the new Sparse Component Analysis (SCA) method extracts efficiently for further usage the underlying evoked auditory potentials masked by strong eye movements.…”

Section: Introductionmentioning

confidence: 99%

Sparse Component Analysis: a New Tool for Data Mining

Georgiev

Theis

Cichocki

et al.

Data Mining in Biomedicine

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In many practical problems for data mining the data X under consideration (given as (m x A^)-matrix) is of the form X = AS, where the matrices A and S with dimensions mxn and nx N respectively (often called mixing matrix or dictionary and source matrix) are unknown (m < n < N). We formulate conditions (SCA-conditions) under which we can recover A and S uniquely (up to scaling and permutation), such that S is sparse in the sense that each column of S has at least one zero element. We call this the Sparse Component Analysis problem (SCA). We present new algorithms for identification of the mixing matrix (under SCAconditions), and for source recovery (under identifiability conditions). The methods are illustrated with examples showing good performance of the algorithms. Typical examples are EEC and fMRI data sets, in which the SCA algorithm allows us to detect some features of the brain signals. Special attention is given to the application of our method to the transposed system X^ = S^ A^ utilizing the sparseness of the mixing matrix A in appropriate situations. We note that the sparseness conditions could be obtained with some preprocessing methods and no independence conditions for the source signals are imposed (in contrast to Independent Component Analysis). We applied our method to fMRI data sets with dimension (128 x 128 x 98) and to EEC data sets from a 256-channels EEC machine.

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