Most stroke patients suffer from a combination of motor and sensory dysfunction and central facial paralysis. Specific rehabilitation training is required to restore those functions. Current research focuses on developing stimulating and straightforward rehabilitation training processes so that patients adhere to the training at home after hospital release. This study proposes enhancing patients’ enthusiasm to participate in facial muscle exercises and improving their postural perception and balance by controlling virtual objects to complete assigned tasks in virtual reality games using different facial expressions with the assistance of haptic feedback. The different rehabilitation exercises for motor, sensory, and facial dysfunctions were combined in one virtual reality game for the first time. The proposed haptic feedback device was modeled, simulated, and characterized. A user study was conducted to validate the proposed system. The experiment result shows that all the designed functions of the comprehensive stroke rehabilitation virtual reality game can be achieved. The added haptic feedback enhances the performance of the aircraft control with facial expressions by lowering the trajectory deviation by 22.57%. This implies that the proposed game may improve users' performance, thus attracting them to conduct more training.

Modern video games are increasingly aiming for more natural interactions and healthier gaming experiences. Haptic devices, in particular, can enhance these experiences by providing multimodal feedback and simulating a variety of body postures. However, limited attention has been paid to utilizing upper limb wearable haptic devices in video games. In. this study, we developed a flight video game that incorporates a wearable pneumatic haptic device. Our designed haptic feedback sleeve can deliver changes in both haptic forces and applied areas on the forearm. The proposed device consists of 40 airbag units made from two layers of TPU film, sealed by heat. To verify its performance, we conducted finite element simulations and experiments to assess the output force, area, and linearity of the airbag units. Two haptic perception experiments were conducted to verify the distribution of this haptic feedback device. Finally, experimental validation combining the flight video game was conducted. The results showed that the distributed upper limb haptic feedback sleeve reduced the user's following angle error by 12.99% when the aircraft roll speed was 16 deg/s. This finding indicates an enhancement in limb motor control ability using the proposed haptic feedback sleeve.

The rapid advancement of brain-computer interface (BCI) technology has created interest in steady-state visual evoked potential (SSVEP)-based BCIs, which are valued for their high information transfer rates and ability to manage multiple targets. Nonetheless, the efficacy of SSVEP decoding is often constrained by the volume and duration of user calibration data, limiting its practical application. Generative adversarial networks (GANs) have shown promise in synthesizing SSVEP electroencephalogram (EEG) data. However, they face challenges, such as low signal-to-noise ratio and capturing both temporal and spatial features. To address the need for high-quality data generation, the Attention-Aided Classifier Wasserstein GAN (AAC-WGAN) is proposed, which is a novel GAN model that combines an Attention Mechanism and an Auxiliary Classifier to improve data quality and classification performance. Our experiments on the Direction and Dial datasets reveal that our model performs obviously better, particularly with a training set ratio of 25% synthetic data and 75% real data. It achieves a classification accuracy of 91.65% on the Direction dataset, which is a significant improvement over the baseline accuracy of 84.32% (p = 0.008) and outperforms other comparable models (p < 0.05 for all comparisons). On the Dial dataset, our model achieves a classification accuracy of 83.48%, outperforming both the baseline of 80.86% and other models. Additional analyses, such as t-SNE visualization and FFT, confirm our model’s effectiveness in feature representation and frequency domain characteristics, with significant improvements in both data quality and classification stability. This work advances the state of SSVEP generation and classification, resulting in significant improvements in data quality and accuracy. It also represents a significant step forward in the development of BCI technologies.