  Recently, surface electromyography (sEMG) emerged as a novel biometric
authentication method. Since EMG system parameters, such as the feature
extraction methods and the number of channels, have been known to affect system
performances, it is important to investigate these effects on the performance
of the sEMG-based biometric system to determine optimal system parameters. In
this study, three robust feature extraction methods, Time-domain (TD) feature,
Frequency Division Technique (FDT), and Autoregressive (AR) feature, and their
combinations were investigated while the number of channels varying from one to
eight. For these system parameters, the performance of sixteen static wrist and
hand gestures was systematically investigated in two authentication modes:
verification and identification. The results from 24 participants showed that
the TD features significantly (p<0.05) and consistently outperformed FDT and AR
features for all channel numbers. The results also showed that the performance
of a four-channel setup was not significantly different from those with higher
number of channels. The average equal error rate (EER) for a four-channel sEMG
verification system was 4% for TD features, 5.3% for FDT features, and 10% for
AR features. For an identification system, the average Rank-1 error (R1E) for a
four-channel configuration was 3% for TD features, 12.4% for FDT features, and
36.3% for AR features. The electrode position on the flexor carpi ulnaris (FCU)
muscle had a critical contribution to the authentication performance. Thus, the
combination of the TD feature set and a four-channel sEMG system with one of
the electrodes positioned on the FCU are recommended for optimal authentication
performance.
