Development of a Bidirectional Brain Machine Interface

描述(申请人提供)：最近的技术进步使使用直接从大脑获得的信号实时移动机器人设备成为可能。脑机接口(BMI)的这一领域可以为瘫痪患者提供运动，为被锁住的患者提供交流，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，并快速准确地监控不断演变的运动。现有的BMI虽然不同凡响，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界面来解决这些限制，该界面以一种更自然的方式产生运动，并通过直接电刺激大脑提供关于运动的反馈。我们的合作伙伴包括西北大学(NU)、芝加哥大学(UC)、马萨诸塞州阿默斯特大学(UMA)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、数学和工程学科的高级学位。米勒(NU)将协调合作关系。他对表现猴子的各种录音、刺激和行为规范都有丰富的经验。Hatsopoulos(UC)处于多电极记录领域的前沿。他是第一个展示由灵长类动物视觉引导的BMI控制的小组的主要成员。巴托(U麻省)在神经网络、机器学习和随机优化方面进行了开创性的研究。费格是控制机器人伸手和抓取机器人的权威，这些机器人学习与环境互动。他们将共同开发大脑活动的解码器，用于引起运动。Romo(UNAM)是研究大脑电刺激诱导的知觉和决策过程的世界领先者。索拉(NU)是神经网络和信息论方面的专家。在Romo的帮助下，她将开发最佳程序，将信息编码到刺激列车中，向大脑提供反馈。Mussa-Ivaldi(NU)将专注于接口的整体设计和评估。他创造了有史以来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.