RQA-BASED ANALYSIS OF SURFACE EMG SIGNALS;

Method of mathematics treatment in the EMG analysis;

Prosthetic Hand Control Based on EMG Signals;

Surface electromyography processing based on recurrence quantification analysis;

We used an algorithm based on single value decomposition to decomposed the signal into tow time- orthogonal subspaces: one contains ECG signal, the other contains artificial noise such as baseline wander and electromyography.

The motion-pattern recognition arithmetic based on s u pport vector machine is proposed to enhance the discrimination rate and the real-time of multi-movement pattern recognition of surface electromyography.

Application of improved BP algorithm to surface EMG signal classification;

Surface EMG Signal Classification Using Wavelet Transform;

Four types of movement of forearm,hand grasp,hand extension,wrist pronation and wrist supination can be identified from surface EMG s.

Identification of Myoelectric signals of the paralysed patient’s leg motions;

The integral absolute value,autoregressive(AR) model coefficients,and linear cepstrum coefficients are extracted as feature parameters from time segments of the surface myoelectric signals.

Statistical characters of the short time slices of the myoelectric signal of musculus flexor carpi ulnaris are preprocessed,then the results are given as the input of self-organization neural network to classify time slices and to find the action start time slice.

To eliminate the noise included in electromyography(EMG),a de-noising method by multi-scale product coefficient hard thresholding was presented.

Aiming at the key issues of the human-computer technology based on biolelectricity, namely the picking up and analysis of bioelectricty signal, we do the research with academically significance and application possibilities of human-computer interactive technology based on electromyography(EMG).