On selecting typical samples in EMG pattern classification / 生物医学工程学杂志

As is well known that the quality of training samples directly influence the recognizing ability of neural network. In this paper, we introduce a method for solving the problem of how to classify the pattern of forearm by obtaining typical samples. At first, the original samples were pretreated by using the membership class function that can improve the quality of cluster sample. Then, the center of clustering could be gained by using the method of clustering and the typical sample was obtained. Based on this method, we can get the typical sample that corresponds with the movements of stretch of arm and fold of arm. We can make them as the training sample of the BP network to classify the pattern of forearm. The experiment indicates that this measure can improve the point of identification.

A Processing Method of EEG Signals Based on Second Generation Wavelet Transform and Blind Signal Separation / 航天医学与医学工程

Objective To study a processing method for EEG signals mixed with EOG and ECG signals disturbance.Methods First,the EEG was denoised by the hard threshold method,the soft threshold method,the compromise threshold method and the ? law threshold method in the second generation wavelet,and then the denoised EEG which still contained EOG and ECG was separated by fast independent component analysis( FastICA) algorithm.Results The ? law threshold method of the second generation wavelet had better denoising effect and FastICA algorithm had more ideal separate performance.Conclusion It is an effective preprocessing method for EEG in denoising with the ? law threshold method of the second generation wavelet and then in separating disturbance of independent source with FastICA algorithm.