Abstract: Independent Component Analysis is a widely used method in EEG data processing for removing unwanted artefacts from the measured data. The drawback of this method is its high computational cost, resulting in long execution times.
A GPU Implementation of FastICA. Code for the ... Includes Python and Jupyter files for a CPU, GPU (CUDA) and a Hybrid version of the FastICA algorithm.
Abstract – Independent Component Analysis is a widely used method in EEG data processing for removing unwanted artefacts from the measured data.
Sep 26, 2019 · We implement a fully GPU FastICA as well as a CPU-GPU hybrid algorithm, both based on the CUDA platform and compare them with the CPU version.
A massively parallel GPU implementation of the popular FastICA algorithm, which can be executed within real-time limits, allowing sophisticated automatic ...
This paper presents an implementation of FastICA, an ICA algorithm, on a multicore GPU. The resulting implementation achieved an overall speedup of 55 for ...
This work presents an optimized implementation of the FastICA algorithm, which is specifically tailored for next-generation GPU architectures such as Nvidia ...
Implementations, such as FastICA, are optimized for parallelization on CPU or first-generation GPU hardware. With the advent of modern, compute centered GPU ...
ABSTRACT. Extracting independent components from audio data has plenty of uses in biology, music, communication and media and in many other fields.
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Nov 29, 2023 · Abstract. High-density electroencephalographic (EEG) systems are utilized in the study of the human brain and its underlying behaviours.