Objective. Brain–computer interface (BCI) control systems monitor neural activity to detect the user’s intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals. Approach. A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user’s gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system’s usability, focusing on its effectiveness and efficiency. Main results. Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system’s feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen’s Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 min to complete the evaluation tasks. The success rate dropped below 0.5 when a 5 min cutoff time was selected. Significance. These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications in the future.

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times ($p<0.001$). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use ($p=0.04$). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

Musculoskeletal disorders caused by poor work posture are a serious concern in the industry since they lead to absenteeism and medical leave from work. In the context of human–robot collaboration, this issue can be mitigated if collaborative robots support human workers to perform their tasks more ergonomically. In this work, we propose a method to optimize human posture during human–robot collaboration using the Particle Swarm Optimization (PSO) algorithm. Our approach involves assigning an appropriate location to the robot’s end-effector to minimize the distance between the optimized posture of the human and their current posture in the working space. To measure human posture, we use the Rapid Entire Body Assessment score (REBA) calculated from body joint angles captured by a Kinect camera. To validate the effectiveness of our proposed method, we conducted a user study with 20 participants in a virtual reality environment. The PSO algorithm could position the robot end-effector to the optimal position close to real time. Our results showed that our method could improve ergonomics by 66%, indicating its potential for use in human–robot collaborative applications.