Artículo

Raimondo, F.; Kamienkowski, J.E.; Sigman, M.; Fernandez Slezak, D. "CUDAICA: GPU optimization of infomax-ICA EEG analysis" (2012) Computational Intelligence and Neuroscience. 2012
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Abstract:

In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card) of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80 of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation. © Copyright 2012 Federico Raimondo et al.

Registro:

Documento: Artículo
Título:CUDAICA: GPU optimization of infomax-ICA EEG analysis
Autor:Raimondo, F.; Kamienkowski, J.E.; Sigman, M.; Fernandez Slezak, D.
Filiación:Departamento de Computación Pabellón i, Ciudad Universitaria, C1428EGA Ciudad Autonoma de Buenos Aires, Argentina
Laboratory of Integrative Neuroscience, Physics Department, University of Buenos Aires, Buenos Aires, Argentina
Palabras clave:Brain computing; Computing time; EEG analysis; Function calls; Independent signals; Multiple channels; On-line analysis; Vector processors; Video cards; Brain computer interface; Matrix algebra; Independent component analysis; algorithm; article; computer graphics; computer program; electroencephalography; human; Internet; signal processing; Algorithms; Computer Graphics; Electroencephalography; Humans; Internet; Signal Processing, Computer-Assisted; Software
Año:2012
Volumen:2012
DOI: http://dx.doi.org/10.1155/2012/206972
Título revista:Computational Intelligence and Neuroscience
Título revista abreviado:Comput. Intell. Neurosci.
ISSN:16875265
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16875265_v2012_n_p_Raimondo

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Citas:

---------- APA ----------
Raimondo, F., Kamienkowski, J.E., Sigman, M. & Fernandez Slezak, D. (2012) . CUDAICA: GPU optimization of infomax-ICA EEG analysis. Computational Intelligence and Neuroscience, 2012.
http://dx.doi.org/10.1155/2012/206972
---------- CHICAGO ----------
Raimondo, F., Kamienkowski, J.E., Sigman, M., Fernandez Slezak, D. "CUDAICA: GPU optimization of infomax-ICA EEG analysis" . Computational Intelligence and Neuroscience 2012 (2012).
http://dx.doi.org/10.1155/2012/206972
---------- MLA ----------
Raimondo, F., Kamienkowski, J.E., Sigman, M., Fernandez Slezak, D. "CUDAICA: GPU optimization of infomax-ICA EEG analysis" . Computational Intelligence and Neuroscience, vol. 2012, 2012.
http://dx.doi.org/10.1155/2012/206972
---------- VANCOUVER ----------
Raimondo, F., Kamienkowski, J.E., Sigman, M., Fernandez Slezak, D. CUDAICA: GPU optimization of infomax-ICA EEG analysis. Comput. Intell. Neurosci. 2012;2012.
http://dx.doi.org/10.1155/2012/206972