High Performance Optimization of Independent Component Analysis Algorithm for EEG Data

Gajos-Balinska, Anna; Wójcik, Grzegorz M.; Stpiczyński, Przemysław

doi:10.1007/978-3-319-78024-5_43

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The future plan is to integrate our solution with tools for EEG signal processing such us NetStation [8][9][10][11]. It is also worth considering the adjustment of the existing solution to the capabilities of a particular architecture to achieve even better results.…”

Section: Discussionmentioning

confidence: 99%

“…It is common that one should use external software to take advantage of the aforementioned method. This prompted the authors of the research to write an application with its own implementation of ICA, integrated with the software used in the Department of Neuroinformatics at Maria Curie-Sklodowska University in Lublin (NetStation) [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Performance comparison of parallel fastICA algorithm in the PLGrid structures

2018

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During processing the EEG signal, the methods of cleaning it from artifacts play an important role. One of the most commonly used methods is ICA (independent component analysis) [1-3]. However, algorithms of this type are computationally expensive. Known implementations of ICA type algorithms rarely include the possibility of parallel computing and do not use the capabilities provided by the architecture itself. This paper presents a parallel implementation of the fastICA algorithm using the available libraries and extensions of the Intel processors (such as BLAS, MKL, Cilk Plus) and compares the execution time for two selected architectures in the PLGrid structure (Zeus and Prometheus).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance comparison of parallel fastICA algorithm in the PLGrid structures

2018

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“…. using the CUBLAS library, which is available through programming in CUDA for the NVIDIA graphics cards [7][8][9][10]. The paper presents improvement that transfers more calculations to the graphics card, thus reducing the calculation time even further.…”

Section: E a R L Y B I R Dmentioning

confidence: 99%

Hybrid implementation of the fastICA algorithm for high-density EEG using the capabilities of the Intel architecture and CUDA programming

Gajos-Balińska,

Wójcik,

Stpiczyński

2023

csci

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High-density electroencephalographic (EEG) systems are utilized in the study of the human brain and its underlying behaviors. However, working with EEG data requires a well-cleaned signal, which is often achieved through the use of independent component analysis (ICA) methods. The calculation time for these types of algorithms is the longer the more data we have. This article presents a hybrid implementation of the fastICA algorithm that uses parallel programming techniques (libraries and extensions of the Intel processors and CUDA programming), which results in a significant acceleration of execution time on selected architectures.

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“…As it was mentioned above, the computations are time consuming. We proposed some methods of the ICA parallelization and tested them on different architectures [20][21][22]. To get satisfactory results, one ought to consider decomposition on the supercomputers or apply at least the CUDA methodology.…”

Section: Independent Component Analysismentioning

confidence: 99%