Hands-Free Mobility Control: A Low-Latency Asynchronous MI-BCI System for Real-Time Robotic Navigation With Cognitive-Ergonomic Enhancement

Individuals with severe motor impairments face significant challenges operating conventional interfaces due to reliance on physical movement. While brain–computer interfaces (BCIs) offer alternative control pathways, traditional paradigms (SSVEP/P300) suffer from stimulus dependency, high latency, and cognitive fatigue, limiting real-world deployment. This study presents a novel asynchronous motor imagery-BCI system for hands-free robotic platform navigation, integrating cognitive ergonomics principles to enhance operator experience. We developed an intuitive four-command control paradigm using a cascaded classifier architecture, eliminating dependence on external triggers. Key innovations include: 1) sub-100 ms ultralow-latency pipeline via hybrid feature fusion and ensemble learning; 2) stimulus-independent operation leveraging endogenous sensorimotor cortex activation (μ/β-band); and 3) cognitive load-optimized interaction with ROS-based neurofeedback and NASA-TLX-validated ergonomic design. Evaluated on BCI Competition IV 2a dataset (nine subjects) and self-collected high-resolution electroencephalography data (seven subjects), the system achieved 72.15% offline classification accuracy and 95.36% online command execution success rate with 97ms mean computational latency. NASA-TLX evaluation revealed a 32% reduction in cognitive workload compared to synchronous paradigms. This work establishes a framework for cognitively enhanced mobility assistance, advancing practical brain-controlled assistive technologies.