Detecting driver fatigue is critical for ensuring traffic safety. Electroencephalography (EEG) is the golden standard for brain activity measurement and is considered a good indicator of detecting driver fatigue. However, the current driver fatigue detection algorithm has limitations in mining and fusing the spatiotemporal characteristics of EEG signals. In this paper, we propose a multi-branch deep learning network named spatio-temporal fusion network with brain region partitioning strategy (STFN-BRPS) to improve the accuracy and robustness of driver fatigue recognition. Initially, we develop a recurrent multi-scale convolution module (RMSCM) comprising a multi-scale convolution sub-module, a CNN-Bi-LSTM sub-module, and a residual structure branch. RMSCM effectively extracts highly discriminative long short-term temporal feature information. Secondly, we propose a dynamic graph convolution module and a spatial graph edges’ importance weight assignment method based on brain region partitioning strategy, which can acquire intrinsic spatial feature information between electrodes. Thirdly, we design a feature fusion module (FFM) that utilizes channel attention to fuse long short-term temporal and spatial features. FFM learns and prioritizes the significance and relevance of each channel in the fused features. Finally, the fused spatio-temporal features are passed into the classification module to obtain the predicted driver fatigue state. Extensive comparison and ablation studies are conducted on EEG signals collected from real-world driving scenarios. The results demonstrate that the proposed STFN-BRPS model delivers superior classification performance compared to the mainstream methods. This study establishes a benchmark for EEG-based driver fatigue detection and related deep-learning modeling work.

The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain–computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient’s motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49%±3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82%±4.66% and 88.48%±5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.

Functional electrical stimulation (FES) can be used to stimulate the lower-limb muscles to provide walking assistance to stroke patients. However, the existing surface electromyography (sEMG)-based FES control methods mostly only consider a single muscle with a fixed stimulation intensity and frequency. This study proposes a multi-channel FES gait rehabilitation assistance system based on adaptive myoelectric modulation. The proposed system collects sEMG of the vastus lateralis muscle on the non-affected side to predict the sEMG values of four targeted lower-limb muscles on the affected side using a bidirectional long short-term memory (BILSTM) model. Next, the proposed system modulates the real-time FES output frequency for four targeted muscles based on the predicted sEMG values to provide muscle force compensation. Fifteen healthy subjects were recruited to participate in an offline model-building experiment conducted to evaluate the feasibility of the proposed BILSTM model in predicting the sEMG values. The experimental results showed that the R ² value of the best-obtained prediction result reached 0.85 using the BILSTM model, which was significantly higher than that using traditional prediction methods. Moreover, two patients after stroke were recruited in the online assisted-walking experiment to verify the effectiveness of the proposed walking-assistance system. The experimental results showed that the activation of the target muscles of the patients was higher after FES, and the gait movement data were significantly different before and after FES. The proposed system can be effectively applied to walking assistance for stroke patients, and the experimental results can provide new ideas and methods for sEMG-controlled FES rehabilitation applications.

In the context of people’s gradually rising consumption levels and the urgent need to upgrade the tourism market, the continuous homogenization of tourism souvenirs is beginning to seriously affect the shopping experience of tourism tourists. This study aims to explore how tourists perceive the phenomenon of the homogenization of tourism souvenirs and how this phenomenon affects tourists’ continuous purchase intention. This study constructs a hypothesis model of the relationship among perceived homogeneity, perceived value, and continuous purchase intention based on a perceived value perspective and validates the model using structural equations. These findings are as follows: (1) Tourists’ perceived product homogeneity and perceived service homogeneity have significant negative effects on perceived product value and continuous purchase intention. (2) Perceived service homogeneity has a significant negative effect on tourists’ perceived emotional value. (3) Perceived product value and perceived emotional value have significant positive effects on continuous purchase intention. The study also explores how to enhance tourists’ perceived value through innovative product design and service system construction. The findings can provide valuable insights for promoting the sustainable development of the tourism market and tourists’ continuous purchase intention.

Su embroidery, as an intangible cultural heritage of China, is a treasure accumulated by human civilization, but it has been gradually fading from people’s view in recent years. To handle the problems of slow creative output, high learning difficulty, and low production efficiency, and to promote the sustainable development of Su embroidery, this study builds an automatic generation system of Su embroidery called MasterSu, based on the CorelDraw platform. The system can automate the generation of embroidery sketches through area texture filling and color recognition, which allows users to participate in the design process. Finally, the performance and usefulness of the system are verified through user experiments, and it is confirmed that the system can facilitate novice users to understand the embroidery culture, learn the embroidery techniques, and create their embroidery works through the system.