Object: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the cross-frequency coupling features between different frequencies have been neglected. Additionally, effectively integrating different neural networks poses challenges for the advanced design of decoding algorithms. Methods: This study proposes a novel end-to-end Multi-Scale Vision Transformer Neural Network (MSVTNet) for MI-EEG classification. MSVTNet first extracts local spatio-temporal features at different filtered scales through convolutional neural networks (CNNs). Then, these features are concatenated along the feature dimension to form local multi-scale spatio-temporal feature tokens. Finally, Transformers are utilized to capture cross-scale interaction information and global temporal correlations, providing more distinguishable feature embeddings for classification. Moreover, auxiliary branch loss is leveraged for intermediate supervision to ensure the effective integration of CNNs and Transformers. Results: The performance of MSVTNet was assessed through subject-dependent (session-dependent and session-independent) and subject-independent experiments on three MI datasets, i.e., the BCI competition IV 2a, 2b and OpenBMI datasets. The experimental results demonstrate that MSVTNet achieves state-of-the-art performance in all analyses. Conclusion: MSVTNet shows superiority and robustness in enhancing MI decoding performance. The source code for MSVTNet is available at https://github.com/SheepTAO/MSVTNet .

The prediction of the remaining useful life (RUL) of wind turbine gearbox bearings is critical to avoid catastrophic accidents and minimize downtime. Temporal convolutional network (TCN), as a potential method of RUL prediction, utilizes dilated causal convolution to extract historic information in the time series, by which it can avoid the disadvantage of long-term dependence faced by classical recurrent neural networks (RNNs). However, a large amount of local information is lost after dilated causal convolution, restricting further improvement of accuracy in RUL prediction or even making TCN invalid when the time series data are not sufficient. To address this issue, an improved TCN denoted as self-calibration temporal convolutional network (SCTCN) is proposed for RUL prediction of wind turbine gearbox bearings, in which the dilated causal convolution of TCN is inherited to extract the long-term historic information, and the self-calibration module is used to focus on the local information in the time series. As a result, SCTCN can learn more complete historic information to improve the accuracy of RUL prediction. Bearing RUL prediction experiments on both test bench and wind turbine gearbox are performed to verify the effectiveness of the proposed method, and the experimental results show that SCTCN has higher prediction accuracy compared with other state-of-the-art methods.