Dimensionality Reduction in EMG-Based Estimation of Wrist Kinematics

Pattern recognition has shown remarkable success in decoding motor information from electromyogram (EMG) signals. To decrease the computational complexity in EMG pattern recognition, it may be useful to reduce the dimensionality of the model input. This paper investigates the effect of reducing the dimensionality of EMG features in a regression-based motion intent estimation model. Ten able-bodied subjects participated in this analytic study. EMG signals from the right forearm and angle of the left wrist in three degrees of freedom (DoF) were measured, concurrently. The TD features were extracted from eight EMG channels, resulting in a total of 32 features. Three dimensionality reduction methods including principal component analysis (PCA), non-negative matrix factorization (NNMF), and canonical correlation analysis (CCA) were applied to the EMG features. Reducing the dimension of the EMG features below a certain threshold degraded the performance of the EMG pattern recognition model. Otherwise, dimensionality reduction did not change the performance. These thresholds for the PCA, NNMF, and CCA methods were 25, 26, and 13, respectively. Based on the results, CCA substantially outperformed PCA and NNMF, as it allowed a significant reduction of the EMG features size, from 32 to 13, with no adverse impact on the performance.