Development of a Bidirectional Brain Machine Interface

描述（由申请人提供）：最近的技术进步使得使用直接从大脑获得的信号来真实的移动机器人装置成为可能。脑机接口（BMI）的这一领域可以为瘫痪患者提供运动，为闭锁患者提供交流，并为全社会更好地了解大脑。为了有效地控制运动，大脑必须能够适当地激活肌肉，并快速准确地监控运动的发展。现有的BMI虽然很出色，但在完成这些任务时，对完整的神经系统的模仿很差。我们提出的工作通过开发一种双向接口来解决这些限制，该接口以更自然的方式产生运动，并通过直接电刺激大脑来提供有关运动的反馈。我们的合作伙伴包括西北大学（NU），芝加哥大学（UC），马萨诸塞大学，阿默斯特大学（UMass）和墨西哥自治大学（UNAM）的成员。合作伙伴拥有生物科学、计算机科学、物理学、数学和工程学科的高级学位。米勒（NU）将协调伙伴关系。他在记录、刺激和行为猴子的行为方案方面拥有丰富的经验。Hatsopoulos（UC）处于多电极记录领域的最前沿。他是第一个展示灵长类动物视觉引导BMI控制的小组的领导成员。Barto（U Mass）在神经网络，机器学习和随机优化方面做了开创性的研究。Fagg（马萨诸塞大学）是控制学习与环境互动的伸手和抓取机器人的权威。他们将共同开发用于引起运动的大脑活动的解码器。Romo（UNAM）是研究大脑电刺激引起的感知和决策过程的世界领导者。Solla（NU）是神经网络和信息理论方面的专家。与Romo一起，她将开发最佳例程，将信息编码到刺激序列中，以向大脑提供反馈。Mussa-Ivaldi（NU）将专注于接口的整体设计和评估。他创造了第一个神经组织和机器人设备之间的双向接口。

DESCRIPTION (provided by applicant): Recent technological advances have made it possible to move a robotic device in real time, using signals obtained directly from the brain. This field of Brain Machine Interface (BMI) has the means to provide movement for paralyzed patients, communication for locked-in patients, and a better understanding of the brain for all of society. In order to control movement effectively, the brain must be able to activate muscles appropriately, and monitor the evolving movement quickly and precisely. Existing BMIs for, while remarkable, do each of these tasks in poor imitation of the intact nervous system. Our proposed work addresses these limitations by developing a bidirectional interface that produces movement in a more natural way, and provides feedback about the movement by direct, electrical stimulation of the brain. Our partnership includes members at Northwestern Univ (NU), Univ of Chicago (UC), Univ of Mass, Amherst (UMass), and the autonomous Univ of Mexico (UNAM). Partners have advanced degrees in a range of biological science, computer science, physics, mathematics, and engineering disciplines. Miller (NU) will coordinate the partnership. He has extensive experience with a wide range of recording, stimulation and behavioral protocols in behaving monkeys. Hatsopoulos (UC) is at the forefront of the field of multi-electrode recordings. He was a leading member of the first group to demonstrate visually guided BMI control by a primate. Barto (U Mass) has done pioneering research in neural networks, machine learning and stochastic optimization. Fagg (UMass) is an authority in the control of reaching and grasping robots that learn to interact with the environment. Together they will develop the decoders of activity from the brain used to cause movement. Romo (UNAM), is a world leader in studies of the perceptual and decision making processes induced by electrical stimulation of the brain. Solla (NU) is an expert in neural networks and information theory. With Romo, she will develop optimal routines to encode information in stimulus trains to provide feedback to the brain. Mussa-Ivaldi (NU) will focus on the overall design and evaluation of the interfaces. He created the first ever bidirectional interface between neural tissue and a robotic device.