ArticleFrontiers in human neuroscience2025
Levels of shared autonomy in brain-robot interfaces: enabling multi-robot multi-human collaboration for activities of daily living.
Article in Frontiers in human neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
- Rethinking Brain-Computer Interfaces for Soft Robotic Systems: A Unified Framework and Perspective.Sensors (Basel, Switzerland) · 2026Article
- Exploring Artificial Intelligence in Orthopedic Surgery: A Review of Perception, Decision, and Execution Systems.Sensors (Basel, Switzerland) · 2026Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Individuals with ALS and other severe motor impairments often rely on caregivers for daily tasks, which limits their independence and sense of control. Brain-robot interfaces (BRIs) have the potential to restore autonomy, but many existing systems are task-specific and highly automated, which reduces the users' sense of empowerment and limits opportunities to exercise autonomy. In particular, shared autonomy approaches hold promise for overcoming current BRI limitations, by balancing user control with increased robot capabilities. In this work, we introduce a collaborative BRI that integrates non-invasive EEG, EMG, and eye tracking to enable multi-user, multi-robot interaction in a shared kitchen environment with mobile manipulators. Our system modulates assistance through three levels of autonomy-Assisted Teleoperation, Shared Autonomy, and Full Automation-allowing users to retain meaningful control over task execution while reducing effort for routine operations. We conducted a controlled user study comparing autonomy conditions, evaluating performance, workload, ease of use, and agency. Our results show that, while Full Automation was generally preferred by users due to lower workload and higher usability, Shared Autonomy provided higher reliability and preserved user agency, especially in the presence of noisy EEG decoding. Although there was significant individual variability in EEG decoding performance, our
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