Brain-controlled robotic arms (BCRAs) have emerged as promising end-effectors in Internet of Things (IoT) environments, enabling direct neural control of physical interactions in remote and complex scenarios. However, existing BCRA systems face challenges in achieving precise fine-grained control and adapting to diverse manipulation tasks. To address these limitations, this study proposes a novel BCRA system with Human-Centered Visual Evoked Potential (HC-VEP) paradigm. By leveraging vision-based spatial mapping, the system enables fine-grained, coordinate-level manipulation of the BCRA in complex 3D environments. To further enhance system performance, Foveal Attention Tracking (FAT) is integrated to rapidly estimate the user's intended grasp location, thereby improving interaction efficiency. Additionally, a Time-Frequency Domain Enhanced Network (TFDE-Net) is developed to improve electroencephalogram (EEG) decoding accuracy through advanced time-frequency feature extraction. Experimental results demonstrate the effectiveness of the proposed system. Offline evaluations show that TFDE-Net achieves a peak information transfer rate (ITR) of 111.81 bits/min, representing a 20.8% improvement over EEGNet. Online experiments in simulated environments demonstrate the efficiency of our paradigm. Specifically, HC-VEP with FAT reduces task completion time by 56.86% compared to HC-VEP without FAT, and by 68.17% compared to traditional SSVEP. Real-world validation experiments with physical robotic arms achieved an overall success rate of 90.0% in unshielded laboratory environments, demonstrating the system's robustness under realistic operating conditions. These findings validate the system's capability for flexible and efficient grasping of arbitrary objects in complex 3D environments, marking a important step toward in practical BCRA applications.

Robotic arm operation based on human–machine collaboration in manufacturing scenarios for the Internet of Things (IoT) has become an important research direction, especially in three-dimensional (3-D) scenarios that require high precision and flexible operation. However, owing to the complexity of operating robotic arms in 3-D scenarios, it is challenging for humans to perform tasks in pilot mode, leading to unnatural human–machine interactions. In this study, an embodied visual evoked potential (E-VEP) paradigm is proposed that can be used to control robotic arms in manufacturing scenarios in embodier mode. In addition, an incremental self-learning intention decoding (ISLID) algorithm is established to address the temporal variability in electroencephalography (EEG) signals. A brain-controlled robotic arm system was developed on the basis of the E-VEP paradigm and the ISLID algorithm. Online free grasping experiments revealed that the task time cost, output delay, and intention output ratio of the proposed system were 89.04 s, 2.22 s, and 46.59%, respectively. Compared with those of brain-controlled robotic arm systems based on the dynamic visual evoked potential (D-VEP) and SSVEP paradigms, the system based on the E-VEP paradigm achieved reductions in the average task time cost of 13.44% and 24.54%, respectively, and reductions in the average intention output ratio of 17.01% and 26.65%, respectively. The proposed brain-controlled robotic arm system holds significant application value in intelligent manufacturing scenarios for the IoT, advancing the integration of brain–machine interfaces and IoT technologies. The video (https://youtu.be/WtRHew4WGyo) demonstrates the utilization process of the proposed brain-controlled robotic arm.

Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16.27 s, an average output latency of 1.54 s, and an average correlation instantaneous rate (CIR) of 0.0321. The proposed system shows 35.37% better efficiency, 8.03% reduced system delay, and 35.28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.

With the rapid advancement of technology, brain-controlled driving (BCD) has emerged as a contemporary focal point of research in academia and industry. BCD refers to the application of brain-machine interface (BMI) technology to driving, where control commands from the human brain are decoded by BMI technology and used to assist in the control of vehicles. However, existing BCD systems display inadequate performance in joint lateral and longitudinal control, and BCD systems in complex driving scenarios with other vehicles have not been studied. In this study, a nonlinear smooth controller is proposed, and a BCD system for complex driving scenarios is developed based on it. First, the BCD system is built from three modules, namely, the vehicle module, the BMI module and the controller module. Subsequently, the nonlinear smooth controller is developed based on the BMI controller, the proximal policy optimization (PPO) controller, and the self-adaptive collaborative (SAC) controller. The SAC controller is designed based on a sigmoid function to achieve nonlinear smoothness in the process of allocating control authority between the PPO controller and the BMI controller. The results of online driving experiments demonstrate that the proposed controller is better equipped to handle complex driving scenarios, exhibiting superior performance, heightened safety, and improved user experience compared to the PPO controller and BMI controller. This study holds significant value in advancing the practicality of BCD and providing a foundation for future research on BMI control.

With the rapid development of the internet of things (IoT) systems, the application potential of remote-oriented unmanned aerial vehicle (UAV) in IoT systems is becoming increasingly prominent. Brain-computer interface (BCI)-based remote-oriented UAV systems can not only leverage the natural advantages of the human brain in cognition and response, but also contribute to safer and more efficient operations in certain special environments. However, remote-oriented BCI systems still face challenges in spatial perception and control capabilities. In this study, a compressed-perceptual visual evoked potentials (CPVEP) paradigm and a human-machine closed-loop (HMCL) controller are proposed for a remote-oriented brain-controlled unmanned aerial vehicle (BCUAV). A BCVAV system for remote application scenarios is constructed based on the CPVEP paradigm and the HMCL controller. Online experiments demonstrates that all subjects have completed the navigation task by the proposed remote-oriented BCUAV system. Human-in-the-loop experiments show that the proposed system can significantly improve the system performance and adaptability of BCUAV to different environments, while significantly reducing the user’s workload. In the future, the proposed remote-oriented BCUAV system can be applied to various scenarios such as remote-controlled search and rescue, traffic monitoring and power line inspection.

OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs. METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated. RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%). SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.