People who are to live, study and work abroad will face more challenges in the new cultural environment and suffer more acculturative stress. Virtual Reality (VR), by which an immersive learning environment can be built, may help them adapt to a foreign culture at a lower cost of time and money. In order to work out a design method for culture learning in VR, we have designed a VR application so that learners can experience and learn the typical western festival culture – Christmas culture – in an immersive environment. To evaluate the effectiveness of the VR method, 50 EFL Chinese university students were enrolled in our experiments and randomly assigned to the VR group and the non-VR group, the data was drawn from cultural knowledge questionnaire, behavior test and Intercultural Sensitivity Scale (ISS). The ANCOVA revealed no major effect for group factor on knowledge learning. Similarly, the Mixed ANOVA identified no major effect for group factor on behavior learning and attitude learning. There was no interaction effect between time and group in all experiments. Our results show that the VR method is preferred by most of the participants, but it shows no remarkable advantage over the non-VR method. Moreover, regression analysis between the culture learning and the sense of presence in VR shows that presence has the potential to improve the performance of intercultural interaction engagement. Our findings are of practical value for culture learning in VR.

Gamification has been widely used in training and enhancing cognitive ability, such as working memory and attention. For example, 3D games can enhance participants' working memory, thereby improving their learning efficiency, and the powerful immersion of VR games can improve the attention of patients with ADHD. However, previous works have not elaborated to what extent the two different game modes can affect cognitive ability respectively, nor have they determined which game mode has a greater effect on the cognitive level. Exploring the impact of different game modes on cognitive ability can help better apply gamification to the training and improvement of cognitive ability, and quantifying cognitive ability through human physiological signals such as brain electrical activity level (EEG) can help evaluate the changes of cognitive ability in different game modes more accurately. Therefore, different from previous studies, we used EEG signals and game performance data (such as game scores and time to complete the game) to calculate participants' cognitive level quantitatively, and did comparative experiments to study cognitive abilities in different game modes and the temporal characteristics of cognitive ability changes. We also explored whether gender would affect cognitive ability. Our research has the following findings. First, compared with the 3D mode, participants get better game scores and higher EEG scores in the VR mode, the time to complete the game task is shorter, and the time to reach the best state of working memory is shorter. Second, participants' gender has no significant effect on working memory, but there is a significant difference in attention in the VR game mode, it takes less time for males to reach the peak of attention than for females. Our conclusion indicates that VR games have greater potential in the field of training and enhancing cognitive ability than 3D games.

Automatic facial expression recognition (FER) is a fundamental topic in computer vision. Many studies have indicated that facial emotion changes are strongly related to certain regions of interest (ROIs), such as the mouth, eyes, eyebrows, and nose; therefore, the features of these facial ROIs are very important for identifying expressions. Since Gabor filters are very efficient in extracting visual content, Gabor orientation filters (GoFs) modulated by Gabor kernels and traditional convolutional filters can capture such ROI information better than conventional convolutional filters. Consequently, this letter presents a light Gabor convolutional network (GCN) consisting of only four Gabor convolutional layers and two linear layers for FER tasks. Extensive experiments on the FER2013, FERPlus and Real-world Affective Faces (RAF) databases demonstrate that the proposed method achieves good recognition accuracy and requires very low computational costs. The source code can be found at https://github.com/general515/Facial_Expression_Recognition_Using _GCN.

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the time-consuming calibration session would increase the visual fatigue of subjects and reduce the usability of the BCI system. The key idea of this study is to propose a cross-subject transfer method based on domain generalization, which transfers the domain-invariant spatial filters and templates learned from source subjects to the target subject with no access to the EEG data from the target subject. The transferred spatial filters and templates are obtained by maximizing the intra- and inter-subject correlations using the SSVEP data corresponding to the target and its neighboring stimuli. For SSVEP detection of the target subject, four types of correlation coefficients are calculated to construct the feature vector. Experimental results estimated with three SSVEP datasets show that the proposed cross-subject transfer method improves the SSVEP detection performance compared to state-of-art methods. The satisfactory results demonstrate that the proposed method provides an effective transfer learning strategy requiring no tedious data collection process for new users, holding the potential of promoting practical applications of SSVEP-based BCI.

Interfaces based on surface electromyography (sEMG) and inertial measurement units (IMUs) enable users to interact with computers in a natural and intuitive way through hand gestures. sEMG or IMU sensor-based approaches can provide muscle electrical activity or motion information to recognize gestures. However, the existing methods recognize static and dynamic gestures separately and hierarchically. In addition, the variability in sEMG data among different subjects limits the performance of sensor-based human–computer interfaces. To address these limitations, this paper proposes a multi-stream convolutional neural network (CNN) architecture with a fine-tuning transfer strategy. The multi-stream architecture explores the complementary nature of sEMG and IMU signals and achieves real-time recognition of dynamic and static gestures in a non-hierarchical way. In addition, a new dataset including seven static gestures and six dynamic gestures is designed for evaluation. The proposed method is verified by experiments on six subjects and is compared with previous methods on the same dataset. Experimental results reveal that the transfer strategy significantly improves recognition accuracy from 89.99% to 98.49%. Meanwhile, by learning the fusion features of sEMG and IMU signals, the recognition accuracy is enhanced from 49.54% (sEMG) and 59.91% (IMU) to 89.99%. In addition, the accuracy and latency of this method for real-time recognition are 98.12% and 103 ms, respectively. These results demonstrate that the proposed multi-stream CNN model accurately recognizes these static and dynamic gestures online in a non-hierarchical way and can effectively utilize the complementarity between sEMG and IMU signals.