With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference (P < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.
Repetitive transcranial magnetic stimulation(rTMS) is a painless and non-invasive method for stimulation and modulation in the field of cognitive neuroscience research and clinical neurological regulation. In this paper, adult Wistar rats were divided into the rTMS group and control group randomly. Rats in the rTMS group were stimulated with 5 Hz rTMS for 14 days, while the rats in the control group did not accept any stimulation. Then, the behavior and local field potentials (LFPs) were recorded synchronously when the rats perform a working memory (WM) task with T-maze. Finally, the time-frequency distribution and coherence characteristics of the LFPs signal in the prefrontal cortex (PFC) during working memory task were analyzed. The results showed that the rats in the rTMS group needed less training days to reach the task correction criterion than the control group (P < 0.05). Compared with the control group, the rTMS group has higher energy (P < 0.01) in θ band (4~12 Hz) and γ band (30~80 Hz). The coherence between the channel pairs decreases as the spatial distance of the channel pairs increases, and the rTMS group exhibits a higher coherence than the control group (P < 0.01). It is concluded that 5 Hz rTMS can improve the excitability of rat prefrontal cortical neurons to a certain extent, and has a positive effect on the working memory ability of normal rats. The results of this paper may provide important theoretical support for further research on the mechanism of action of rTMS on WM.
Affective brain-computer interfaces (aBCIs) has important application value in the field of human-computer interaction. Electroencephalogram (EEG) has been widely concerned in the field of emotion recognition due to its advantages in time resolution, reliability and accuracy. However, the non-stationary characteristics and individual differences of EEG limit the generalization of emotion recognition model in different time and different subjects. In this paper, in order to realize the recognition of emotional states across different subjects and sessions, we proposed a new domain adaptation method, the maximum classifier difference for domain adversarial neural networks (MCD_DA). By establishing a neural network emotion recognition model, the shallow feature extractor was used to resist the domain classifier and the emotion classifier, respectively, so that the feature extractor could produce domain invariant expression, and train the decision boundary of classifier learning task specificity while realizing approximate joint distribution adaptation. The experimental results showed that the average classification accuracy of this method was 88.33% compared with 58.23% of the traditional general classifier. It improves the generalization ability of emotion brain-computer interface in practical application, and provides a new method for aBCIs to be used in practice.