Cooperation of CUDA and Intel multi-core architecture in the independent component analysis algorithm for EEG data
-
Anna Gajos-Balińska
,
Grzegorz M. Wójcik
und
Przemysław Stpiczyński
Abstract
Objectives
The electroencephalographic signal is largely exposed to external disturbances. Therefore, an important element of its processing is its thorough cleaning.
Methods
One of the common methods of signal improvement is the independent component analysis (ICA). However, it is a computationally expensive algorithm, hence methods are needed to decrease its execution time. One of the ICA algorithms (fastICA) and parallel computing on the CPU and GPU was used to reduce the algorithm execution time.
Results
This paper presents the results of study on the implementation of fastICA, which uses some multi-core architecture and the GPU computation capabilities.
Conclusions
The use of such a hybrid approach shortens the execution time of the algorithm.
Research funding: None declared.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Conflict of interest: The authors declare that they have no conflict of interest.
Informed consent:Informed consent was obtained from all individuals included in this study.
Ethical Approval: The conducted research is not related to either human or animal use.
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Artikel in diesem Heft
- Research Articles
- Overview of the holographic-guided cardiovascular interventions and training – a perspective
- Development of the low-cost, smartphone-based cardiac auscultation training manikin
- Cooperation of CUDA and Intel multi-core architecture in the independent component analysis algorithm for EEG data
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- Recognition of multifont English electronic prescribing based on convolution neural network algorithm
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