Due to the sparsity of brain encoding, the neural ensemble signals recorded by microelectrode arrays contain a lot of noise and redundant information, which could reduce the stability and precision of decoding of motion intent. To solve this problem, we proposed a decoding method based on partial least squares (PLS) feature extraction in our study. Firstly, we extracted the features of spike signals using the PLS, and then classified them with support vector machine (SVM) classifier, and decoded them for motion intent. In this study, we decoded neural ensemble signals based on plus-maze test. The results have shown that the proposed method had a better stability and higher decoding accuracy, due to the PLS combined with classification model which overcame the shortcoming of PLS regression that was easily affected by accumulated effect of noise. Meanwhile, the PLS method extracted fewer features with more useful information in comparison with common feature extraction method. The decoding accuracy of real data sets were 93.59%, 84.00% and 83.59%, respectively.
In order to solve the problem that the early onset of paroxysmal atrial fibrillation is very short and difficult to detect, a detection algorithm based on sparse coding of Riemannian manifolds is proposed. The proposed method takes into account that the nonlinear manifold geometry is closer to the real feature space structure, and the computational covariance matrix is used to characterize the heart rate variability (RR interval variation), so that the data is in the Riemannian manifold space. Sparse coding is applied to the manifold, and each covariance matrix is represented as a sparse linear combination of Riemann dictionary atoms. The sparse reconstruction loss is defined by the affine invariant Riemannian metric, and the Riemann dictionary is learned by iterative method. Compared with the existing methods, this method used shorter heart rate variability signal, the calculation was simple and had no dependence on the parameters, and the better prediction accuracy was obtained. The final classification on MIT-BIH AF database resulted in a sensitivity of 99.34%, a specificity of 95.41% and an accuracy of 97.45%. At the same time, a specificity of 95.18% was realized in MIT-BIH NSR database. The high precision paroxysmal atrial fibrillation detection algorithm proposed in this paper has a potential application prospect in the long-term monitoring of wearable devices.