Energy-efficient FastICA implementation for biomedical signal separation

Lan-Da Van*, Di You Wu, Chien Shiun Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

This paper presents an energy-efficient fast independent component analysis (FastICA) implementation with an early determination scheme for eight-channel electroencephalogram (EEG) signal separation. The main contributions are as follows: 1) energy-efficient FastICA using the proposed early determination scheme and the corresponding architecture; 2) cost-effective FastICA using the proposed preprocessing unit architecture with one coordinate rotation digital computer-based eigenvalue decomposition processor and the proposed one-unit architecture with the hardware reuse scheme; and 3) low-computation-time FastICA using the four parallel one-units architecture. The resulting power dissipation of the FastICA implementation for eight-channel EEG signal separation is 16.35 mW at 100 MHz at 1.0 V. Compared with the design without early determination, the proposed FastICA architecture implemented in united microelectronics corporation 90 nm 1P9M complementary metal-oxide-semiconductor process with a core area of 1.221×1.218 mm2 can achieve average energy reduction by 47.63%. From the post-layout simulation results, the maximum computation time is 0.29 s.

Original languageEnglish
Article number6032107
Pages (from-to)1809-1822
Number of pages14
JournalIEEE Transactions on Neural Networks
Volume22
Issue number11
DOIs
StatePublished - 1 Nov 2011

Keywords

  • Blind source separation
  • electroencephalogram
  • energy efficiency
  • fast independent component analysis
  • hardware implementation

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