RI-Small: Statistical Decoding Models to Improve the Performance of Motor Cortical Brain-Machine Interfaces

RI-Small：提高运动皮质脑机接口性能的统计解码模型

• 批准号: 0916154
• 负责人: Wei Wu
• 金额: $ 13.56万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916154&HistoricalAwards=false
• 关键词: RI; Small; Statistical; Decoding; Models

The goal of this project is to develop statistical models to accurately and efficiently decode population neuronal activity in the motor and premotor cortex. The study focuses on motor behavior as it is easily measured and strongly correlated with neuronal activity. Recent advances in motor cortical brain-machine interfaces have shown that research animals and paralyzed human patients were able to perform rudimentary actions with external devices such as robotic limbs and computer cursors. Neural decoding, which provides control commands to external devices, plays a key role in such interfaces by converting brain signals (e.g., spiking rates of a population of neurons) to kinematic states (e.g., hand position, hand movement direction).Current decoding models are often based on the strong assumption that the neural signal sequence is a stationary process. This assumption, however, does not take into account the significant dynamic variability of spiking activity over time. Moreover, these methods have either focused on decoding the entire trajectory or on the occurrence times of a few "landmarks" during the movement. Effective coupling of these two complementary strategies can be expected to improve the decoding performance by better exploiting the nature of the landmark-defined movement. This project will develop computational methods to address these two issues. For the non-stationarity, the research team will develop adaptive versions of state-of-the-art decoding methods such as particle filters and point process filters that can capture the varying patterns in neural signals and update the model accordingly. To couple trajectory decoding and time decoding, landmark times will be identified from the neural activity, and then incorporated into the kinematic model. The team will use simultaneous recordings from multi-electrode arrays in the primary motor cortex, the dorsal premotor cortex, and the ventral premotor cortex that were recorded during behavior or visuo-motor tasks. Improved decoding methods are expected to have significant impacts on neural prosthetics.

该项目的目的是开发统计模型，以准确有效地解码电动机和前皮层中的人群神经元活动。 该研究的重点是运动行为，因为它很容易测量并与神经元活性密切相关。 运动皮质脑机界面的最新进展表明，研究动物和瘫痪的人类患者能够使用外部设备（例如机器人四肢和计算机光标）进行基本作用。 神经解码为外部设备提供控制命令，在此类界面中起着关键作用，通过将脑信号（例如神经元人群的尖峰速率）转换为运动态（例如，手动位置，手动运动方向）。经常基于强有力的假设神经信号序列是一个站点过程。 但是，这一假设并未考虑到随着时间的推移峰值活动的显着动态变异性。此外，这些方法要么重点是解码整个轨迹，要么是在运动过程中的一些“地标”的发生时间。 可以预期，这两种互补策略的有效耦合可以通过更好地利用地标定义运动的性质来提高解码性能。 该项目将开发计算方法来解决这两个问题。 对于非平稳性，研究团队将开发自适应版本的最先进的解码方法，例如粒子过滤器和点过程过滤器，它们可以捕获神经信号中的不同模式并相应地更新模型。为了将轨迹解码和时间解码，将从神经活动中确定里程碑时间，然后纳入运动学模型。 该团队将使用主电动机皮层，背部前皮层的多电极阵列中的同时记录，以及在行为或视觉运动任务中记录的腹侧前马皮皮质。 预计改进的解码方法将对神经假体产生重大影响。