Subtle electromyographic pattern recognition for finger movements: A Pilot study using BSS techniques

Ganesh R. Naik, Dinesh K. Kumar

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax).

Original languageEnglish
Article number1250078
Number of pages19
JournalJournal of Mechanics in Medicine and Biology
Volume12
Issue number4
DOIs
Publication statusPublished - Sep 2012
Externally publishedYes

Keywords

  • Blind source separation
  • gestures
  • source separation
  • subtle finger movements
  • surface electromyography

Fingerprint Dive into the research topics of 'Subtle electromyographic pattern recognition for finger movements: A Pilot study using BSS techniques'. Together they form a unique fingerprint.

Cite this