• the Korean Society for Brain and Neural Sciences


Original Research

Exp Neurobiol 2010; 19(1): 54-61

Published online June 30, 2010

© The Korean Society for Brain and Neural Sciences

Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

Kyung‐Jin You, Ki‐Won Rhee and Hyun‐Chool Shin*

Department of Electronic Engineering, College of IT, Soongsil University, Seoul 156‐743, Korea

Correspondence to: *To whom correspondence should be addressed.
TEL: 82-2-828-7165, FAX: 82-2-821-7653


We provide a novel method to infer finger flexing motions using a four-channel surface electromyogram (EMG). Surface EMG signals can be recorded from the human body non-invasively and easily. Surface EMG signals in this study were obtained from four channel electrodes placed around the forearm. The motions consist of the flexion of five single fingers (thumb, index finger, middle finger, ring finger, and little finger) and three multi.finger motions. The maximum likelihood estimation was used to infer the finger motions. Experimental results have shown that this method can successfully infer the finger flexing motions. The average accuracy was as high as 97.75%. In addition, we examined the influence of inference accuracies with the various arm postures.

Keywords: surface EMG, finger motions, neural signal processing, HCI