Pei Yu

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation. Here, we proposed a DA method for the motor imagery EEG signal called brain-area-recombination. For the BAR, each sample was first separated into two ones by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification. To make a comparative investigation, we selected two common DA methods, and the BAR method beat them. Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM, note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI.

A Higher education system is closely related to a country’s development. Governments need to make appropriate adjustments to ensure the healthy and sustainable development of higher education. To measure and evaluate the development status of the national higher education system and propose a set of policies, the following work has been done in this paper: First, this paper establishes a comprehensive evaluation index system based on the AHP-FCE model. The healthy development of education in significant countries was compared horizontally and vertically. The results of weighted scores were used to determine the Japanese higher education system as the research object. Secondly, this paper uses factor analysis and principal component analysis to evaluate the level of sustainable development of higher education, detecting development barriers that migrate from the current to the future. This paper proposes a healthy and sustainable development policy blueprint from four aspects. Finally, this article uses the ARMA time series model to predict the development trend of Japanese higher education in the next 30 years and formulates a timetable for corresponding implementation arrangements. In addition, this paper quantitatively evaluates the effectiveness of the policies we propose and their impact on reality and provides references for other countries in the world.