Advanced Algorithms for Neural Prosthetic Systems

我们看似毫不费力就能做出协调动作的能力，掩盖了在我们神经系统中起作用的复杂计算机制。近年来，神经科学领域的数据采集技术和实验的复杂性急剧增加。这项技术的发展创造了大量有价值的实验数据，但深入研究这些数据所需的分析方法尚未开发出来。与此同时，过去十年在计算统计学、数据分析技术和机器学习领域取得了重大进展。在这些领域的研究使调查和理解以前无法解释的数据成为可能。该提案旨在汇集这两个领域的关键研究，以显著推进神经义肢系统在科学和医学上的重要应用，从而大大改善全球数十万严重残疾人类患者的生活质量。像肌萎缩性侧索硬化症这样的衰弱性疾病会使人失去自主运动控制能力。然而，在大多数情况下，大脑本身保持完整并具有正常功能。导致严重瘫痪的脊髓损伤也是如此。事实上，四瘫患者将“恢复对手臂/手的控制”列为改善生活质量的首要任务，因为恢复这一功能将使患者获得重大的独立性。为了解决这个问题，神经假肢系统寻求访问大脑中的信息，并利用这些信息来控制假肢设备，如机械臂或计算机光标。在开发这样一个系统的过程中，有许多医学、科学和工程方面的挑战，但所有的神经假肢系统都有一个共同的解码算法。解码算法将神经活动映射为物理命令，例如控制机械臂的参数。目前的解码方法已经显示出令人兴奋的概念证明，但是在该领域生产出具有可与健康人类手臂相媲美的速度和准确性的临床可行的假肢设备之前，必须解决许多缺点。我们的研究项目将使用先进的统计和机器学习技术来创建算法，以更高的精度解码神经活动。我们已经确定了几个有意义的改进机会，从纳入自然到达的统计数据到在现实的在线环境中验证这些算法。总的来说，这些算法的发展应该有助于创造一个更高质量的神经假肢装置。

Our seemingly effortless ability to make coordinated movements belies the sophisticated computational machinery at work in our nervous system. In recent years, the field of neuroscience has been dramatically expanding the complexity of its data acquisition technologies and experiments. This technological development has created a preponderance of valuable experimental data, but the analytical methods required to deeply interrogate this data have not yet been developed. Simultaneously, the last decade has seen major advances in the fields of computational statistics, data analysis techniques, and machine learning. Research in these areas has enabled investigation into and understanding of previously uninterpretable data.This proposal seeks to bring together key research from these two fields to significantly advance the scientifically and medically important application of neural prosthetic systems, which seeks to improve greatly the quality of life of hundreds of thousands of severely disabled human patients worldwide. Debilitating diseases like Amyotrophic Lateral Sclerosis can leave a human without voluntary motor control. However, in most cases, the brain itself remains intact and has normal function. The same is true with spinal cord injuries that result in severe paralysis. In fact, tetrapalegic patients list ``regaining arm/hand control'' as the top priority for improving their quality of life, as regaining this function would allow significant patient independence. To address this priority, neural prosthetic systems seek to access the information in the brain and use that information to control a prosthetic device such as a robotic arm or a computer cursor. There are many medical, scientific, and engineering challenges in developing such a system, but all neural prosthetic systems share in common a decoding algorithm. Decoding algorithms map neural activity into physical commands such as parameters for controlling a robotic arm. Current decoding approaches have shown exciting proofs of concept, but there are a number of shortcomings that must be addressed before the field produces a clinically viable prosthetic device with speed and accuracy comparable to a healthy human arm. Our research programme will use advanced statistical and machine learning technologies to create algorithms that can decode neural activity with higher precision that previously seen. We have identified several opportunities for meaningful improvement, from incorporating the statistics of natural reaching to validating these algorithms in a realistic online setting. Taken together, these algorithmic developments should help create a much higher quality neural prosthetic device.