Zhong Guo Chan Ye Jing Ji Xin Xi Wang·2025-06-25 02:26Core Viewpoint - MicroCloud Hologram (NASDAQ: HOLO) has successfully developed an innovative robotic arm control system that integrates augmented reality (AR), computer vision, and steady-state visual evoked potential (SSVEP)-brain-computer interface (BCI), marking a significant step towards practical brain-controlled robotics [1][20] Group 1: System Features - The ARC-BCI control system allows users to control the robotic arm through electroencephalogram (EEG) signals, utilizing AR environments provided by HoloLens [1] - The AR environment is customized for specific application scenarios to ensure intuitive user interfaces and natural interactions [1][13] - Machine learning algorithms automatically adjust AR displays to accommodate different user perspectives and operating habits [2] Group 2: Signal Processing and Synchronization - Signal preprocessing techniques such as filtering and denoising are employed to enhance the quality of EEG signals [3] - Statistical and machine learning methods are used to identify and extract key features related to SSVEP [4] - A timestamp synchronization mechanism ensures precise alignment between BCI signal acquisition and AR environment stimulation [5] Group 3: Feedback and Control Mechanisms - A feedback system is established to relay the status information of the robotic arm back to the AR interface, allowing users to adjust their next actions [6] - An efficient motion planning algorithm is implemented to quickly respond to user BCI commands [9] - Force feedback sensors are integrated to provide feedback signals, enhancing operational safety and precision [10] Group 4: System Testing and Validation - Independent testing of AR, BCI, computer vision, and robotic arm control modules is conducted [11] - The overall system performance and reliability are validated through simulations of actual operational scenarios [12] Group 5: User Interface and Accessibility - A simple and intuitive user interface (UI) is designed to reduce the learning curve for users [13] - The system interface is made accessible to users of varying abilities and backgrounds [14] Group 6: Data Management and Performance Monitoring - Key data during operations are logged for subsequent analysis and system optimization [15] - Real-time performance monitoring is implemented to quickly identify and resolve potential issues [16] Group 7: Safety Enhancements - A system-level fault detection mechanism is established to ensure timely responses to issues [18] - Strict user permission management is set up to prevent unauthorized operations [18] Group 8: Future Applications - The ARC-BCI system is expected to enhance the practicality of brain-controlled robotics and provide new directions for future human-computer interface designs, with potential applications in healthcare, manufacturing, and service industries [20]