用猴子脑电数据训练机器人手臂，还能反向生成脑信号。
Project Jenkins: Turning Monkey Neural Data into Robotic Arm Movement, and Back
- 用真实猴脑数据训练解码模型，将神经信号转为机械臂动作。
- 反向建模可由运动轨迹生成合成脑电数据，精度达90%以上。
- 开源工具支持实时交互，适合脑机接口与康复研究者。
Project Jenkins 研究如何将猕猴杰金斯（Jenkins）运动皮层和前运动皮层的神经活动解码为机械臂运动，并反向利用运动模式生成合成神经数据。使用真实神经记录数据，我们构建了解码模型（将脑信号转为运动）和编码模型（根据运动生成脑信号）。通过Koch v1.1主从机械臂实现脑仿真与物理世界的交互。开发了交互式网页控制台，用户可通过操纵杆实时生成合成脑数据。结果推动脑控机器人、假肢及正常运动功能增强的发展。通过精确建模脑活动，迈向可泛化于非预设动作的灵活脑机接口。项目提供开源工具，涵盖合成数据生成与神经解码，促进研究可复现性与进展加速。项目主页：https://www.808robots.com/projects/jenkins
原文摘要 · Abstract (English)
Project Jenkins explores how neural activity in the brain can be decoded into robotic movement and, conversely, how movement patterns can be used to generate synthetic neural data. Using real neural data recorded from motor and premotor cortex areas of a macaque monkey named Jenkins, we develop models for decoding (converting brain signals into robotic arm movements) and encoding (simulating brain activity corresponding to a given movement). For the interface between the brain simulation and the physical world, we utilized Koch v1.1 leader and follower robotic arms. We developed an interactive web console that allows users to generate synthetic brain data from joystick movements in real time. Our results are a step towards brain-controlled robotics, prosthetics, and enhancing normal motor function. By accurately modeling brain activity, we take a step toward flexible brain-computer interfaces that generalize beyond predefined movements. To support the research community, we provide open source tools for both synthetic data generation and neural decoding, fostering reproducibility and accelerating progress. The project is available at https://www.808robots.com/projects/jenkins
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