EFRI-M3C: Development of New Algorithmic Models and Tools to Enhance Neural Adaptation in Brain Computer Interface Systems.

EFRI-M3C：开发新的算法模型和工具以增强脑机接口系统中的神经适应。

• 批准号: 1137211
• 负责人: Daniel Moran
• 金额: $ 199.25万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1137211&HistoricalAwards=false
• 关键词: EFRI; M3C; Development; New; Algorithmic

Human interaction with machines has always relied on some form of muscle movement to translate the brain's desired action to the machine (e.g. turning a knob). The objective of our project is to eliminate the need for muscle transformations in the man-machine interface. Using a new brain-computer interface (BCI) technology (electrocorticography or ECoG) pioneered by the research team, we will develop novel decoding algorithms to control the force/torque inputs to an external device directly. Likewise, by designing machine learning algorithms to identify and incorporate neural plasticity in the decoding schemes will allow the BCI to evolve over time. Finally, by combining brain signals from multiple areas to identify various brain states, we can identify and change the effectors to be controlled. Intellectual Merit: All previous BCI studies decoded only kinematic signals to control computer cursors as well as robotic limbs. While kinematic control is a natural extension for disabled individuals trying to regain function lost by paralysis or amputation, a direct interface between the brain and the machine allows for much more elegant interaction. Thus, rather than controlling a robotic arm through the use of imagined self limb movements (a proxy of intention), one rather controls the device as if it was a part of their own body. For instance, mapping brain activity to kinetic parameters such as a robot?s torque motor allows the individual to directly control the forceful interactions within the system. Broader Impact: One advantage of ECoG is that while it is an invasive recording technology, the electrodes can be placed epidurally which significantly reduces the risk profile for implantation. In the long term, this should allow ECoG-based BCIs to be accepted as a viable implant in able-bodied humans. Developing a safe and effective BCI modality for the general public will fundamentally change how humans interact with machines. No longer will humans require muscle activity as an intermediary to interact with machines. A whole new field of man-machine interfacing will be initiated where the brain builds complex internal models of the machine's dynamics (instead of musculoskeletal dynamics) for accurate and direct control of the machine's effectors.

人类与机器的互动一直依赖于某种形式的肌肉运动，以将大脑想要的动作转化为机器（例如转动旋钮）。我们项目的目标是消除在人机界面中对肌肉转换的需要。使用研究团队开创的新的脑机接口（BCI）技术（皮层电图或ECoG），我们将开发新的解码算法来直接控制外部设备的力/扭矩输入。 同样，通过设计机器学习算法来识别并将神经可塑性纳入解码方案，将允许BCI随着时间的推移而发展。最后，通过结合来自多个区域的大脑信号来识别各种大脑状态，我们可以识别和改变要控制的效应器。 智力优势：所有以前的BCI研究都只解码了控制计算机光标和机器人肢体的运动信号。虽然运动控制是残疾人试图恢复因瘫痪或截肢而失去的功能的自然延伸，但大脑和机器之间的直接接口允许更优雅的互动。因此，人们不是通过使用想象的自身肢体运动（意图的代理）来控制机器人手臂，而是控制设备，就好像它是他们自己身体的一部分。例如，将大脑活动映射到机器人等动力学参数？的转矩电机允许个人直接控制系统内的有力的相互作用。更广泛的影响：ECoG的一个优点是，虽然它是一种侵入性记录技术，但电极可以放置在脑内，这大大降低了植入的风险。从长远来看，这将使基于ECoG的BCI被接受为健全人类的可行植入物。为公众开发安全有效的BCI模式将从根本上改变人类与机器的交互方式。人类将不再需要肌肉活动作为与机器交互的中介。一个全新的人机接口领域将被启动，其中大脑构建机器动力学（而不是肌肉骨骼动力学）的复杂内部模型，以准确和直接控制机器的效应器。