Next generation brain-machine interfaces controlled synergistically with artificial intelligence

项目总结 美国有500多万人--几乎每50人中就有1人--生活在瘫痪状态，原因包括 中风、脊髓损伤、多发性硬化症和肌萎缩侧索硬化症。失去行动能力的人也会失去 他们在生活中有深刻的控制感、自由感和独立性。但瘫痪并不会夺走一个人的 移动的意图或欲望；大脑仍然通过神经信号编码这些想法。脑机接口 (BMI)旨在通过将这些神经信号转化为行动来恢复与世界沟通的能力。 BMI将神经信号解码为计算机光标在屏幕或机械臂上的移动，从而允许 用户可以自主地与世界互动。 虽然BMI已经存在了20多年，但它们早在2004年就一直处于试点临床试验阶段， 没有得到广泛的使用。造成这种情况的关键原因是，BMI的表现还没有达到 克服成本和风险的性能。这对两种需要神经外科手术的侵袭性BMI来说都是正确的，因为 以及非侵入性的BMI，它可以在不进行手术的情况下使用，但性能较低。 这项建议旨在通过广泛的辅助措施来显著提高数百万瘫痪患者的生活质量。 fiBMI在未来几年内可用。为了实现这一开创性目标，需要有一个范例 改变了BMI的基本运作方式。革命性的新一代BMI显著影响fi必须 旨在：(1)实现当前BMI系统所不可能实现的前所未有的性能 (2)将患者的成本降至最低，最好是非侵入性的。恩- 需要非常新颖的BMI体系结构才能从根本上超越性能与成本之间的权衡，实现 以更低的风险和成本实现卓越的性能。 为了满足这一需求，我建议开发下一代BMI，其中用户和人工智能(AI) 智能体协同合作。我们称其为“AI-BMI”。AI代理预测用户的预期运动动作 协同作用有助于完成它们。重要的是，AI代理帮助精确而详细地执行 用户的预期动作，增强性能。通过这样做，这种体系结构发生了根本性的变化 BMI的设计目标从精确动作的神经解码(difficult)到行为和神经 对用户意图的推断(更容易)。非侵入式人工智能-BMI，如果成功，将是变革性的，使 下一代BMI技术，允许瘫痪患者再次行动，但降低医疗风险 与神经外科相关，并降低系统成本。

PROJECT SUMMARY Over 5 million people in the USA – nearly 1 person in 50 – live with a form of paralysis due to causes including stroke, spinal cord injury, multiple sclerosis, and ALS. People who have lost the ability to move also lose a profound sense of control, freedom, and independence in their lives. But paralysis does not take away one's intent or desire to move; the brain still encodes these thoughts through neural signals. Brain-machine interfaces (BMIs) aim to restore the ability to communicate with the world by translating these neural signals into actions. BMIs decode neural signals into the movements of a computer cursor on a screen or a robotic arm, allowing the user to interact with the world autonomously. While BMIs have existed for over two decades, they have remained in pilot clinical trials dating back to 2004 and have not achieved widespread use. The key reason for this is that BMI performance has not achieved levels of performance that overcome their costs and risks. This is true for both invasive BMIs requiring neurosurgery, as well as for non-invasive BMIs, which can be used without surgical procedures but achieve low performance. This proposal aims to dramatically increase the quality of life for millions with paralysis by making widely accessi- ble BMIs available within the next five years. To achieve this groundbreaking goal, there needs to be a paradigm shift in the way BMIs fundamentally operate. Revolutionary next generation BMIs making significant impact must be designed to: (1) achieve categorically unprecedented performance not possible with current BMI systems (e.g., over an order of magnitude improvement) and (2) minimize cost to patients, ideally being non-invasive. En- tirely novel BMI architectures are needed to fundamentally transcend a performance vs cost trade-off, achieving excellent performance at lower risks and costs. To meet this need, I propose to develop a next-generation BMI where the user and an artificial intelligence (AI) agent synergistically cooperate. We term this an “AI-BMI.” The AI agent predicts the user's intended motor actions and synergistically helps to complete them. Critically, the AI agent aids the precise and detailed execution of the user's intended movements, augmenting performance. By doing so, this architecture fundamentally changes the design objectives of BMIs from neural decoding of precise movements (difficult) to behavioral and neural inference of the user's intent (easier). Non-invasive AI-BMIs, if successful, would be transformative, enabling next generation BMI technology that allows people with paralysis to once again move, but mitigates medical risks associated with neurosurgery and lowers system costs.