Brain-computer interface (BCI) establishes a direct information communication channel between the brain and the external environment, which has increasingly highlighted its important scientific significance and application value in many aspects of human motor function rehabilitation, replacement and enhancement. As the most natural brain-computer interaction mode, BCI paradigm based on movement intention has been widely concerned by researchers. The decoding of pre-movement EEG patterns can make BCI more responsive and flexible. However, the EEG features at this stage are weak and difficult to recognize efficiently. Based on the above challenges, research is focused on the enhancement of temporal and spatial lateral characteristics of pre-movement EEG in low frequency. Aleft and right hand self-paced keystroke task is designed, and a new composite spatial filtering algorithm is proposed by combining discriminative canonical pattern matching (DCPM) and task-related component analysis (TRCA). The results show that the average accuracy of EEG signal recognition under this algorithm can reach 78.56%, which is better than previous literature reports, and can provide theoretical basis and technical support for efficient brain-computer interaction based on motion intention.
As a new human-computer interaction technology, brain-computer interface (BCI) has become a research hotspot in the field of science and technology, and its application field is constantly expanding, which has great demand for high-speed communication technologies such as 5G and 6G. Given this demand, this paper starts with an overview of BCI and 5G/6G technologies, and analyzes the specific needs of BCI technology in a 5G/6G communication environment. Then, the application scenarios of BCI combined with high-speed communication technologies such as 5G/6G are discussed. Finally, a BCI end-to-end architecture with 5G/6G as the communication core is proposed to build a BCI modular system, in order to provide a new solution for technology iteration and industrial application in the field of BCI.
As a vital element in brain-computer interface (BCI) technology, sensors serve as the front devices for electroencephalogram acquisition and regulation. Among them, the minimally invasive cortical BCI sensors can attach epidurally or subdurally without penetrating into the brain tissue, ensuring the safe implantation. These sensors are typically employed to collect signals from specific cortical areas including movement, vision, and language, which open up possibilities for various brain functions and general applications like cortical rehabilitation, as well as the focus of scientific research and clinical application. Therefore, focusing on the current development status of minimally invasive BCI sensors in both business and research domains, and analyzing and looking forward to future technologies and application trends, can provide reference and inspiration for the academic and industrial fields of BCI.
Although existing EEG compression algorithms can achieve good compression rates, they lack attention to task-related data and are also unable to meet the real-time requirements of brain-computer interface (BCI) applications, which will significantly reduce the performance of BCI systems. Based on the subspace method, when the task-related information of the BCI system is known, the task related EEG signals can be preserved as much as possible during the compression process, which can significantly reduce the amount of data that needs to be transmitted without affecting the performance of the BCI system. By using a finite impulse response filter bank to approximate the signal subspace of task related components, the EEG signal can be segmented into the smallest possible compression ratio and processed in real-time. On the premise that there is no significant difference in the performance of some classification algorithms compared to the original data, an algorithm that only transmits 8% of the data can be proposed. This algorithm can not only transmit less data while minimizing the impact on the performance of the BCI system, but also achieve real-time compression, whitch has important application value.
The basic hardware components of the brain-computer interface, such as electroencephalogram (EEG) acquisition chips or EEG collectors, are gradually being implemented. It is necessary to use an analog signal generator to test and verify the performance. A high-precision microvolt-level EEG signal analog generation system is proposed to address the issues of complexity, bulkiness, and low accuracy in traditional signal generation systems. After experimental verification, this system can generate microvolt-level sine waves, square waves, triangular waves, and EEG signals with a voltage generation error within ±1 μV. This meets the performance testing requirements of EEG acquisition chips or EEG collectors and provides a reference for research in brain-computer interface technology.
As the aging population and the number of patients with chronic diseases increase, the field of rehabilitation medicine faces both significant challenges and opportunities. Non-invasive brain computer interface (BCI) technology, as an innovative diagnostic and therapeutic tool, brings new hope to patients with motor disabilities caused by central nervous system damage such as stroke. Compared to traditional rehabilitation training, single-modal BCI technology has the advantage of activating sensory-motor cortex and assisting patients’ rehabilitation from a brain-level perspective, but each single-modal paradigm has its limitations. Hybrid modalities have been a recent research focus, aiming to improve the overall system’s reliability and accuracy by combining two modalities. Based on this, a novel motor-visual hybrid modality BCI technology is proposed, with an average classification accuracy of 86.67% in online experiments. It can simultaneously evoke responses from the motor cortex and visual cortex, and is expected to become a new paradigm for active rehabilitation and neural reshaping.
Brain-computer interface (BCI) is a cutting-edge direction in the intersection and integration of artificial intelligence (AI) and neuroscience. Its development is at a turning point from scientific research to the market, and it has become a strategic battleground for countries and regions to seize the “new track” of future industries. Based on the leading advantages in research and transformation of brain-computer integration technology, Zhejiang Province has conducted research on the cutting-edge achievements of BCI development, as well as the foundation and shortcomings of Zhejiang Province’s BCI development. This paper analyzed the development trends of key areas, and proposed seizing opportunities to accelerate the layout. Around areas such as neural circuit analysis, BCI chips and electrode materials, brain-computer intelligent fusion systems, and brain-computer medical applications, policy support has been strengthened, major platforms have been created, leading enterprises have been cultivated, scene applications have been accelerated, and multi cross collaboration has been promoted to transform the “0-100” advantage into a “1-100” advantage. The BCI industry should be cultivated as a leading industry in the future.
With the advancement of the information age, brain-computer interface (BCI) technology has developed rapidly, and its application prospects in medical, auxiliary communication and other fields have attracted much attention. Firstly, an overview of the development history and current status of BCI technology was provided, and the morphological changes and future development of this technology were analyzed. The ethical issues brought about by this technology were discussed. Secondly, the ethical challenges of artificial intelligence ethics, biomedical ethics, and cross-border ethics to BCI technology were analyzed, and the impact of these challenges on social policies and governance was demonstrated. Finally, a rational regulatory path was proposed to address the ethical challenges of BCI technology, aiming to provide legal and moral guidance for the healthy development of BCI technology through multidimensional strategies such as legislation, technological governance, and ethical education. The comprehensive analysis shows that a comprehensive and multi-level rational regulatory model is an effective response to the ethical challenges of BCI technology.
Brain-computer interface (BCI) is an interdisciplinary field combining brain science, information and communication technology, and artificial intelligence, enabling a direct information exchange between the brain and external devices. After decades of theoretical research and engineering validation, BCI is gradually moving from scientific research to industrial applications. With the increasing interest in the innovation chain and industrial chain, BCI has received widespread attention from various interdisciplinary fields. However, there are certain differences in the concepts of BCI technology in areas such as paradigm encoding and algorithm decoding compared with those in artificial intelligence and information and communication fields, which has caused some limitations on cooperation and exchange among multiple fields. To further promote the cross-field cooperation and exchange of BCI, and to drive the coordinated development of the technical chain and innovation chain in this field, this paper explores the paradigms, algorithms, as well as basic concepts and principles of encoding and decoding of BCI. Then, it designs a steady-state visual evoked potentials BCI based on left and right visual field stimulation.
In recent years, as one of the key technology paths for data secure circulation, privacy-preserving computing technology has developed rapidly, and it has been widely used in finance, government affairs, medical care, and other scenarios. However, with the development of current privacy-preserving computing technology products, the cross-platform interconnection between privacy-preserving computing platforms with different system architectures or functions has gradually attracted attention. This poses new challenges to the large-scale application of privacy-preserving computing technology. Focusing on this key issue, this paper deeply analyzes the industry’s development from the perspectives of conceptual connotation, implementation path, practical cases, standards, and specifications. It also deeply discusses the future development direction of cross-platform interconnection.
At present, the development level of digital economy of countries along the Belt and Road Initiative is generally low, which seriously restricts the effective cooperation and common development between China and countries along the Belt and Road Initiative in the field of digital economy. This paper focuses on the supply capacity of digital economy in the countries along the Belt and Road Initiative, estimates and comparative analyzes the supply capacity of digital economy of these countries through building a quantitative evaluation index system for the supply capacity of digital economy and puts forward countermeasures and suggestions for the next step of cooperation and development between China and these countries in the field of digital economy based on the measurement results.
Currently, the trend of intelligent production has emerged, making the full connectivity of production elements a necessary path. To achieve the connectivity of production elements, it is necessary to break the information silos and enable interconnection among different manufacturers, and to realize intelligent decision-making and control on the industrial site. To this end, China Telecom has proposed the Time-Determined Network and Intelligent Control Converged System architecture, which integrates industrial PON time-determined network, open converged automated control system, and AI system, achieving closed-loop management in five aspects: network, data, control, AI, and business.
Intelligent policing is the advanced development stage of public security informatization. It is also the core means of policing mode upgrading, which promotes the transformation from post-passive disposal response to pre-active prediction and early warning in crime prevention, risk control and social governance. Based on the vein of iot sensing-cloud network convergence-data basic support-application service-security assurance-operation, this paper proposes a set of panoramic and systematic intelligent policing system solution to promote the construction of policing informatization in various places.