Nonlinear Principal Component Analysis for Feature Extraction of SEMG
In connection with the character of Surface Electromyography signal (SEMG) a new method that uses nonlinear Principal Component Analysis (NLPCA) to extract feature from SEMG was proposed. After filtering SEMG, it utilizes NLPCA to achieve data compression, which transforms multi way SEMG to one dimensional feature data saying principal component, and then,outputs the extraction in principal curve.NLPCA basing on auto associative neural networks was utilized to extracted principal component from two way SEMG, which derived from ulnar extensor muscle and ulnar flexor muscle of wrist respectively. Experimental results showed that, after processing SEMG of four hand motion patterns that including fist clenching, fist unfolding, wrist intorsion and wrist extortion with this method, principal curves with good character of category division were produced. According to the shape features of principal curves, motion of hand can be recognized efficiently.