SUBTLE ELECTROMYOGRAPHIC PATTERN RECOGNITION FOR FINGER MOVEMENTS: A PILOT STUDY USING BSS TECHNIQUES

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).

[1]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[2]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[3]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[4]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[5]  George Coulouris,et al.  Supporting gestural input for users on the move , 2003 .

[6]  Andreas Ziehe,et al.  Artifact Reduction in Magnetoneurography Based on Time-Delayed Second Order Correlations , 1998 .

[7]  Christopher J James,et al.  Independent component analysis for biomedical signals , 2005, Physiological measurement.

[8]  E. Oja,et al.  Independent Component Analysis , 2013 .

[9]  Subramaniam Parasuraman,et al.  ROBOT-ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING GA PROCESS , 2011 .

[10]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[11]  Antoine Souloumiac,et al.  Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..

[12]  Sridhar P. Arjunan,et al.  FRACTAL PROPERTIES OF SURFACE ELECTROMYOGRAM FOR CLASSIFICATION OF LOW-LEVEL HAND MOVEMENTS FROM SINGLE-CHANNEL FOREARM MUSCLE ACTIVITY , 2011 .

[13]  Ian Oakley,et al.  Tilt and Feel: Scrolling with Vibrotactile Display , 2004 .

[14]  Tzi-Dar Chiueh,et al.  Learning algorithms for neural networks with ternary weights , 1988, Neural Networks.