The motor imagery electroencephalography (MI-EEG) reflects the subjective motor intention, which has received increasing attention in rehabilitation. How to extract the features of MI-EEG accurately and quickly is the key to its successful application. Based on the analysis and comparison of the existing feature extraction algorithms, a feature extraction method based on principal component analysis (PCA) and deep belief networks (DBN) is proposed, namely PCA-DBN. Firstly, the second-order moment is used to analyze the time-domain of MI-EEG, select the effective time interval. Secondly, PCA is used to analyze the selected time-domain interval and obtain the principal component feature points. Then, feature points are imported into DBN to realize the final feature extraction. Finally, use the softmax classifier to complete task classification. Perform algorithm validation on the BCI Competition II Data set III and BCI Competition IV Data sets 2b, classification accuracies are 96.25% and 91.71%, kappa values are 0.925 and 0.8342. The paired-sample t-test with FDR correction is carried out on the verification results, and the comparison with some better classification algorithms shows that the algorithm has better performance. In the end, this method is used to extract the features of laboratory data, the optimal classification accuracy is 97.69% and kappa value is 0.9538, the validity of the method is further verified. INDEX TERMS Deep belief networks, motor imagery electroencephalogram, principal component analysis, second-order moment, softmax classifier.
The parallel metamorphic mechanism is a novel metamorphic mechanism. Its kinematics analysis is not only a new research field, but also a difficult problem. In this paper, with the origin of coordinates on the moving platform describing its position and Euler angles describing its orientation, we present the quaternion expression of an arbitrary point's location on the moving platform in the fixed coordinate system. Then a method to establish the unified mathematical model for kinematics analysis of the parallel metamorphic mechanism is presented. Based on this unified mathematical model, forward and inverse kinematics analysis of the parallel metamorphic mechanism in different configurations can be conducted. Using kinematics analysis of a novel 4-URU parallel metamorphic mechanism, the proposed kinematics analysis method is further elaborated, and the resultant elimination method is presented to solve the kinematics equations. Finally, a numerical example is given to verify that the proposed method is correct and effective.
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