title: A canonical correlation analysis based EMG classification algorithm for eliminating electrode shift effect.
abstract: Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.
authors.lastname: Zhe Fan
authors.lastname: Zhong Wang
authors.lastname: Guanglin Li
authors.lastname: Ruomei Wang
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D006225:Hand; D006801:Humans; D009038:Motion; D018763:Muscle Fatigue / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D014953:Wrist