Anna Gajos-Balińska, Grzegorz M. Wójcik, Przemysław Stpiczyński
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引用次数: 0
Abstract
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.
高密度脑电图(EEG)系统用于研究人脑及其潜在行为。然而,处理脑电图数据需要对信号进行很好的净化,这通常需要通过使用独立分量分析(ICA)方法来实现。数据越多,这类算法的计算时间就越长。本文介绍了 fastICA 算法的混合实现,它使用了并行编程技术(英特尔处理器的库和扩展以及 CUDA 编程),从而显著加快了在选定架构上的执行时间。