A novel recognition and classification approach for motor imagery based on spatio-temporal features.

Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

The role of anger rumination as a mediator in the relationship between driver moral disengagement and driving angry.

Background: Road rage is a common phenomenon during driving, which not only affects the psychological health of drivers but also may increase the risk of traffic accidents. This article explores the impact of moral disengagement and anger rumination on road rage through two studies. Method: This research combined experimental studies with survey questionnaires. Study one used a driving simulator to investigate whether moral disengagement and anger rumination are psychological triggers of road rage in real-time driving, and whether there are differences in the main psychological triggers of road rage under different road scenarios. Building on the first study, study two employed a survey questionnaire to analyze the relationship between moral disengagement, anger rumination, and road rage. Participants in both studies were drivers with certain driving ages and experience. Data were processed and analyzed using descriptive statistics, factor analysis, reliability and validity tests, and multiple regression analysis. Results: The findings indicated: (1) There were significant differences in the anger induction rate across different road scenarios, χ2 = 35.73, p < 0.01, effect size = 0.29. Significant differences in average anger levels were observed in scenarios involving oncoming vehicles, lane-cutting, sudden stops by the vehicle ahead, pedestrians crossing the road, and traffic congestion (F = 20.41, p < 0.01, ηp2 = 0.36), with anger rumination playing a major role in the formation of road rage; (2) Moral disengagement significantly predicted road rage (ß = 0.25, t = 3.85, p < 0.01). The predictive effect of moral disengagement on anger rumination was significant (ß = 0.39, t = 6.17, p < 0.01), as was the predictive effect of anger rumination on road rage (ß = 0.43, t = 6.3, p < 0.01). The direct effect of moral disengagement on road rage included 0 in the bootstrap 95% confidence interval, while the mediating effect of anger rumination did not include 0 in the bootstrap 95% confidence interval, indicating that anger rumination fully mediated the relationship between moral disengagement and road rage.

An impedance biosensor array for label-free detection of multiple antigen-antibody reactions.

An electrochemical impedance biosensor array with protein-modified electrodes was designed and fabricated in this report. To demonstrate its feasibility of the detection of multiple antigen-antibody binding reactions based on a label-free approach, human IgG (hIgG), rat IgG (rIgG), human globin and bovine serum albumin were immobilized, respectively, on the gold electrodes and then the resultant array was incubated with goat anti-hIgG, goat anti-rIgG, anti-human globin antibody and the mixture of three antibodies, respectively. The results indicated that the electron transfer resistance of the electrodes was significantly changed due to formation of the antigen-antibody conjugated layer. In addition, experimental conditions such as the protein concentration for the immobilization and screen were studied and optimized. Furthermore, the surface of various protein-modified electrodes was imaged with atomic force microscopy and the height distribution of protein particles was obtained with the Particle Analysis Software. The relative results were fully in accordance with the ones from the electrochemical impedance spectroscopy.