Brain-Computer Interfaces (BCIs) are currently developing rapidly and their application scope is very wide. However, although the BCI is developing very rapidly at the technical level, there is a big gap in the training of BCI talents. The BCI teaching system based on distributed architecture realizes the functions of algorithm submission, evaluation and feedback by integrating theory and practice, and achieves the effect of helping students to check learning results in real time and supporting teachers to provide targeted guidance. The system adopts modular design, supports cloud and local deployment, and utilizes message middleware and registry to achieve efficient communication and scheduling. By lowering the learning threshold and enhancing the practice opportunities, the system effectively solves the problems of insufficient practice and homogeneous evaluation model in the teaching of BCI, and provides important support for the training of BCI talents.

Brain-Computer Interface (BCI) technology enables human-computer interaction by directly connecting the brain with external devices, and has recently demonstrated extensive application scenarios in healthcare, education, industry, and other fields. While significantly improving patients’ quality of life, BCI technology also raises a series of ethical concerns. Firstly, this paper introduces the development history and application domains of BCI technology. Secondly, it analyzes the ethical risks brought by BCI technology from individual, societal, and moral perspectives, summarizing the influencing factors of these risks from technical and human aspects. Thirdly, it outlines the characteristics of ethical issues in BCI technology and reviews the ethical requirements for BCIs in China. Finally, it proposes considerations for ethical review in BCI technology.

With the widespread application of Electroencephalogram (EEG) signal processing technology in medical, neuroscience, and human-computer interaction fields, traditional single-machine architectures face challenges such as limited computational resources, high latency, and poor stability in high-concurrency scenarios. This paper proposes a high-performance EEG signal processing cluster architecture based on Nginx load balancing. By leveraging Nginx’s reverse proxy technology, concurrent requests are distributed to distributed nodes, combined with standardized data processing workflows and a unified cluster entry point, significantly improving real-time performance of system, resource utilization, and throughput. Experimental results demonstrate that under 100~2 000 concurrent loads, the cluster architecture achieves notable optimizations in both average and minimum response time, along with a substantial increase in throughput. Additionally, Nginx’s IP access control strategy effectively blocks unauthorized requests, ensuring data security. The architecture enables efficient resource scheduling through dynamic load balancing, providing robust technical support for large-scale neural data processing. Future work will integrate artificial intelligence and edge computing technologies to extend the architecture to complex EEG analysis scenarios, advancing precision medicine and intelligent interaction development.

Current motor imagery brain-computer interface (MI-BCI) rehabilitation training methods suffer from a lack of diversity in experimental approaches, insufficient real-time feedback optimization, and low decoding rates of electroencephalogram (EEG) signals. This paper innovatively proposes an MI-BCI rehabilitation training method to address these problems, integrating concentration assessment and feedback optimization. A paradigm of EEG signals acquisition is explored by incorporating concentration level assessment. The efficiency of decoding motor imagery EEG signals is significantly improved, benefiting from the feedback of concentration assessment of the subjects in a task state. Experimental results demonstrate that the proposed novel MI-BCI rehabilitation training method improves the concentration levels of subjects, and simultaneously validates the differences in EEG signals with varying degree of concentration. Furthermore, an MI-EEG binary classification accuracy of 84.37% is achieved.

With the development of science and technology, more and more physiological signal analysis devices have been applied in medical fields, which can effectively enhance people’s health. The assessment and testing verification of medical devices face challenges such as high technical complexity and long cycles. Therefore, physiological signal generators have become a hot topic for research and development in recent years. These devices are designed for the functional performance testing and calibration of physiological signal analysis devices, and thus have extremely high application value. There is currently a lack of summary of physiological signal simulators from the principle to the technical solution in the field, and there is a great difference between different physiological signals. Therefore, this paper first systematically elaborates the characteristics of physiological signals, the architecture of physiological signal simulators and the principles of their sub-modules. Then, it compares and analyzes the design schemes and application scenarios of mainstream signal generators. Finally, it systematically reviews the implementation schemes and innovation points of existing physiological signal simulators, providing some reference for the research and application of physiological signal simulators.

During the 14th Five-Year Plan period and in its Future Industrial Cluster Planning, Shenzhen has identified brain science and Brain-Computer Interface (BCI) engineering as one of its eight future industries, with the potential to evolve into a strategic emerging industry. Based on detailed industry and scientific research data, this paper focuses on the integration of “four chains” (industrial chain, innovation chain, capital chain, and talent chain). By employing bibliometric analysis, patent analysis, field studies and other methods, it systematically examines the industrial development, innovation capacity, market dynamics, and talent structure in this field, proposing actionable suggestions for cultivation and development. This field concentrates on three directions: understanding the brain, protecting the brain, and interfacing with the brain. The global ecosystem spans from fundamental research to commercial applications. While China demonstrates expanding demand, aggregated capital, and emerging enterprise clusters, it still faces several challenges. In recent years, Shenzhen has leveraged its policy-capital-market synergy to achieve remarkable growth in scientific output and cross-regional collaboration, positioning itself as a pivotal hub in this field.

Brain-Computer Interface (BCI), as a transformative technology, achieves the assistance, enhancement, and restoration of human physiological functions by establishing communication and control channels directly between the brain and external devices. In recent years, with the rapid development of fields such as neuroscience and artificial intelligence, BCI technology is gradually moving from the laboratory to clinical applications and commercialization. This paper systematically reviews the classification, development trends, and current industrial development status of BCI technology, and makes predictions on future trends. At the technical level, significant progress has been made in both the communication and movement BCIs, and the therapeutic BCIs, including innovation in non-invasive and invasive technologies. In terms of industrial development, governments around the world have launched brain plan strategic planning to guide BCI’s development direction. At the same time, investment and financing activities are becoming increasingly active, and the layout of the industrial chain is constantly improving. It is expected that a number of BCI industry clusters and excellent enterprises will emerge in the future, promoting the accelerated development of the industry. BCI technology is expected to be widely applied in fields such as medical rehabilitation, education, and industry, reshaping the paradigm of human-computer interaction.

With the development of brain-computer interface technology, emotion recognition based on Electroencephalogram(EEG) signals has become a research hotspot. This paper compares the performance of different kernel functions in Support Vector Machine(SVM) for EEG-based emotion recognition tasks, and contrasts them with common machine learning methods such as decision trees, random forests, and neural networks. Based on the DEAP dataset, by analyzing the performance of various kernel functions (linear, radial basis function, and polynomial) and other models, this paper found that random forest achieves the best performance in terms of accuracy and AUC values. Linear kernel SVM is suitable for linearly separable data, while radial basis function and polynomial kernels show relatively poorer performance. Additionally, this paper explores the performance of neural networks and proposes future research directions for optimizing models and kernel function selection. It aims to provide valuable insights into EEG-based emotion recognition and advance the development of BCI technology.

The report to the “20th National Congress of the Communist Party of China” proposed to establish an overall safety and emergency response framework. First, the connotation and characteristics of information and communication safety and emergency are put forward. Then, the current situation of information and communication safety system and emergency system are analyzed, an overall information and communication safety and emergency response framework is proposed, elements of each pillar are described, and the interrelationships of each pillar are expounded. Finally, construction suggestions for each pillar system are provided.

The digital twin city has become an important path for city-wide digital transformation as the digitization process continues to accelerate and develop in depth. The urban digital twin fuses massive cross-industry data, making it possible for the city’s operating mechanism to be presented in real time in digital space and deduced in advance. Accompanied by the explosive development of generative Artificial Intelligence(AI), new technology application scenarios and new modes such as AI Generated Content, Agent, and embodied intelligence continue to emerge. In the field of digital twin city, AI has shown a strong enabling role in 3D data generation, multimodal data input and output, group intelligence in digital space, and single body intelligence in physical space. And AI has initially formed the enabling role and path of seven aspects, including twin rapid modeling, 3D data automatic generation, rapid scene creation, intelligent decision-making, algorithmic scenario autonomy, multimodal data processing, and virtual and real interaction control. It helps to promote the high-level advancement of city-wide digital transformation.

With the rapid development of Artificial Intelligence (AI) technology, the introduction of AI-based design into 5G air interface design has become an important direction for the future evolution of 5G. The establishment of 5G wireless AI dataset is a key step for introducing AI technology into 5G air interface design. This paper provides a systematic analysis of the establishment of 5G wireless AI dataset. Firstly, it elaborates the basic theories and methods involved in establishing 5G wireless AI dataset. Then, based on the latest progress in 3GPP research, it conducts an analysis and demonstration on the methods for establishing datasets for several typical use cases, including AI-based Channel State Information (CSI) feedback, beam management and positioning enhancement. Finally, it proposes the prospects for the continuous evolution of 5G wireless AI datasets.

By analyzing the relational characteristics among different entities such as APPs, SDKs, and developers, a multi-entity knowledge graph is designed and implemented. This approach proposes relationship mining and correlation analysis methods based on the multi-entity knowledge graph, offering new research perspectives and technical solutions for typical security scenarios such as user privacy data flow and risk APP traceability.