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Brain–computer interface control with artificial intelligence copilots

2025-09-01 15:11:54 英文原文

作者:Kao, Jonathan C.

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摘要

This document appears to be a research paper or article focused on advancements in brain-computer interfaces (BCIs) and assistive robotics, particularly the concept of shared autonomy where humans and robots collaborate by sharing control tasks. The paper cites several studies that contribute to this field, highlighting key developments such as: 1. **Shared Autonomy in Robotics**: Studies like "Shared autonomy with learned latent actions" [Jeon et al., 2020] discuss methods for integrating human input with robotic systems to enhance task performance through shared control. 2. **Neuroprosthetics and Speech Decoding**: Research such as "A high-performance speech neuroprosthesis" [Willett et al., 2023], "Large-scale single-neuron speech sound encoding across the depth of human cortex" [Leonard et al., 2024], and "An accurate and rapidly calibrating speech neuroprosthesis" [Card et al., 2024] explore advanced neurotechnologies for speech restoration in individuals with paralysis, using both invasive and non-invasive methods. 3. **BCI Control Enhancements**: Studies like "Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces" [Dangi et al., 2014] focus on improving BCI control efficiency by implementing adaptive algorithms that learn from user feedback to enhance system responsiveness and accuracy. 4. **Robotics Datasets and Models**: Contributions like "HARMONIC: a multimodal dataset of assistive human-robot collaboration" [Newman et al., 2022], "DROID: a large-scale in-the-wild robot manipulation dataset" [Khazatsky et al., 2024], and the Open X-Embodiment Collaboration datasets provide essential resources for advancing research in collaborative robotics and BCI systems. 5. **Visual and Language Guidance**: Papers like "Diffusion policy: visuomotor policy learning via action diffusion" [Chi et al., 2024] and related works on vision-language-action models (e.g., RT-1, RT-2) explore how visual cues and language inputs can be integrated to improve robotic task execution. 6. **Non-invasive Neuromotor Interfaces**: Innovations such as "A generic non-invasive neuromotor interface for human–computer interaction" [Kaifosh et al., 2025] present new technologies that could democratize access to advanced BCIs, making them more accessible and less invasive. 7. **Hybrid BCI Systems**: Studies like "Semi-autonomous robotic arm reaching with hybrid gaze-brain machine interface" [Zeng et al., 2019] demonstrate the potential of combining multiple input modalities (e.g., eye tracking and EEG) to enhance user control over assistive devices. The paper also includes references to software tools used for MEG/EEG data processing, such as the MNE software suite [Gramfort et al., 2014], which are crucial for analyzing neurodata in BCIs. Overall, this document highlights significant advancements and emerging trends in integrating BCI technology with robotics to enhance human-robot collaboration, improve accessibility, and advance neuroprosthetics.