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This paper proposes an improved centered cycle method for identifying the Preisach distribution function based on the non-uniform discretization. The principle and the limits of the centered cycle method are first introduced. Then, the non-uniform discretization method is proposed based on the curvature of the anhysteretic magnetization curve. The numbers of loops for identifying the distribution function are decided by the proportion of the integral of curvature. The method to determine the cell values of the non-uniformly discrete Preisach triangle is also introduced. The proposed method is validated by the laboratory test of a toroidal sample. The results show the higher precision of the proposed method for predicting the hysteresis loop than the centered cycle method by the same number of loops used for identifying. [ABSTRACT FROM AUTHOR]

Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain's intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What's more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG signals' advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.