Implementation of pipelined FastICA on FPGA for real-time blind source separation

Kuo Kai Shyu*, Ming Huan Lee, Yu Te Wu, Po Lei Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

Fast independent component analysis (FastICA) algorithm separates the independent sources from their mixtures by measuring non-Gaussian. FastICA is a common offline method to identify artifact and interference from their mixtures such as electroencephalogram (EEG), magnetoencephalography (MEG), and electrocardiogram (ECG). Therefore, it is valuable to implement FastICA for real-time signal processing. In this paper, the FastICA algorithm is implemented in a field-programmable gate array (FPGA), with the ability of real-time sequential mixed signals processing by the proposed pipelined FastICA architecture. Moreover, in order to increase the numbers precision, the hardware floating-point (FP) arithmetic units had been carried out in the hardware FastICA. In addition, the proposed pipeline FastICA provides the high sampling rate (192 kHz) capability by hand coding the hardware FastICA in hardware description language (HDL). To verify the features of the proposed hardware FastICA, simulations are first performed, then real-time signal processing experimental results are presented using the fabricated platform. Experimental results demonstrate the effectiveness of the presented hardware FastICA as expected.

Original languageEnglish
Pages (from-to)958-970
Number of pages13
JournalIEEE Transactions on Neural Networks
Volume19
Issue number6
DOIs
StatePublished - 2008

Keywords

  • Blind source separation (BSS)
  • Fast independent component analysis (FastICA)
  • Field-programmable gate array (FPGA)
  • Floating point (FP)

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