Neuro signal has many more advantages than myoelectricity in providing information for prosthesis control, and can be an ideal source for developing new prosthesis. In this work, by implanting intrafascicular electrod...Neuro signal has many more advantages than myoelectricity in providing information for prosthesis control, and can be an ideal source for developing new prosthesis. In this work, by implanting intrafascicular electrode clinically in the amputee’s upper extremity, collective signals from fascicules of three main nerves (radial nerve, ulnar nerve and medium nerve) were suc- cessfully detected with sufficient fidelity and without infection. Initial analysis of features under different actions was performed and movement recognition of detected samples was attempted. Singular value decomposition features (SVD) extracted from wavelet coefficients were used as inputs for neural network classifier to predict amputee’s movement intentions. The whole training rate was up to 80.94% and the test rate was 56.87% without over-training. This result gives inspiring prospect that col- lective signals from fascicules of the three main nerves are feasible sources for controlling prosthesis. Ways for improving accu- racy in developing prosthesis controlled by neuro signals are discussed in the end.展开更多
基金Project (No. 39930070) supported by the National Natural Science Foundation of China
文摘Neuro signal has many more advantages than myoelectricity in providing information for prosthesis control, and can be an ideal source for developing new prosthesis. In this work, by implanting intrafascicular electrode clinically in the amputee’s upper extremity, collective signals from fascicules of three main nerves (radial nerve, ulnar nerve and medium nerve) were suc- cessfully detected with sufficient fidelity and without infection. Initial analysis of features under different actions was performed and movement recognition of detected samples was attempted. Singular value decomposition features (SVD) extracted from wavelet coefficients were used as inputs for neural network classifier to predict amputee’s movement intentions. The whole training rate was up to 80.94% and the test rate was 56.87% without over-training. This result gives inspiring prospect that col- lective signals from fascicules of the three main nerves are feasible sources for controlling prosthesis. Ways for improving accu- racy in developing prosthesis controlled by neuro signals are discussed in the end.
