Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced ‘fine-tune’ to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and ‘fine-tune’ transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.

The electronic structures, DOSs, carrier transport properties, and optical properties of CsGeX₃ (X = I, Br or Cl) perovskites under triaxial strains of −4% to 4% were investigated based on density functional theory.

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.

Low-toxicity, air-stable cesium bismuth iodide Cs₃Bi₂X₉ (X = I, Br, and Cl) perovskites are gaining substantial attention owing to their excellent potential in photoelectric and photovoltaic applications. In this work, the lattice constants, band structures, density of states, and optical properties of the Cs₃Bi₂X₉ under high pressure perovskites are theoretically studied using the density functional theory. The calculated results show that the changes in the bandgap of the zero-dimensional Cs₃Bi₂I₉, one-dimensional Cs₃Bi₂Cl₉, and two-dimensional Cs₃Bi₂Br₉ perovskites are 3.05, 1.95, and 2.39 eV under a pressure change from 0 to 40 GPa, respectively. Furthermore, it was found that the optimal bandgaps of the Shockley–Queisser theory for the Cs₃Bi₂I₉, Cs₃Bi₂Br₉, and Cs₃Bi₂Cl₉ perovskites can be reached at 2–3, 21–26, and 25–29 GPa, respectively. The Cs₃Bi₂I₉ perovskite was found to transform from a semiconductor into a metal at a pressure of 17.3 GPa. The lattice constants, unit-cell volume, and bandgaps of the Cs₃Bi₂X₉ perovskites exhibit a strong dependence on dimension. Additionally, the Cs₃Bi₂X₉ perovskites have large absorption coefficients in the visible region, and their absorption coefficients undergo a redshift with increasing pressure. The theoretical calculation results obtained in this work strengthen the fundamental understanding of the structures and bandgaps of Cs₃Bi₂X₉ perovskites at high pressures, providing a theoretical support for the design of materials under high pressure.

We demonstrate the direct generation of visible vortex beams (LG₀₁ mode) from a doughnut-shaped diode-pumped Pr:YLF laser. In continuous-wave mode, the maximum vortex output power was 36 mW at 523 nm, 354 mW at 607 nm, 838 mW at 639 nm, 722 mW at 721 nm, respectively. Moreover, based on this operation, the orange and red passively Q-switched vortex lasers were also achieved by inserting a Co:MgAl₂O₄ crystal into the laser cavity as a saturable absorber. The shortest pulse width of Q-switched vortex laser was 58 ns for 607 nm, and 34 ns for 639 nm, respectively. Our work provides a reliable and efficient method for the direct generation of visible vortex lasers for potential applications.