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首页> 美国卫生研究院文献>Frontiers in Neuroscience >A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent
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A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent

机译：使用支持向量机和深度神经网络解码运动意图的脑机接口性能折衷的表征

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Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies–a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his performance to that of able-bodied participants performing the same task, establishing a quantitative target for ideal BCI-FES performance on this task. Cumulatively, these results help quantify BCI decoder performance characteristics relevant to potential users and the complex interactions between them.
机译：脑计算机接口（BCI）系统的实验室演示通过将神经活动直接与辅助设备的控制联系起来，有望减少与瘫痪相关的残疾。潜在用户的调查显示了针对此类系统的临床翻译的几个关键BCI性能标准。在这些标准中，高精度，短响应时间和多功能性是BCI系统的神经解码组件（将神经活动转化为控制信号的算法）直接影响的三个关键特征。建立同时满足这三个准则的解码器很复杂，因为针对一个准则进行优化可能会导致其他准则发生不良变化。不幸的是，迄今为止，几乎没有工作来量化解码器设计如何同时影响这些性能特征。在这里，我们使用两种不同的解码策略系统地探讨了离散运动分类的准确性，响应潜伏期和多功能性之间的取舍–支持向量机（SVM）分类器代表了当前离散运动的最新技术实验室演示中的运动分类和建议的深度神经网络（DNN）框架。我们利用了人类四肢瘫痪研究参与者的皮质内记录，他们想象自己会进行几种不同的手和手指运动。对于这两种解码器，我们发现响应时间会增加（即反应变慢），而精度会随着功能数量的增加而降低。但是，我们还发现，与SVM相比，DNN的响应时间增加和精度下降以及附加功能的减少都较小。我们还表明，数据预处理步骤可能以截然不同的方式影响两个解码器的性能特征。最后，我们使用DNN解码器实时评估了四肢瘫痪参与者的表现，以控制其瘫痪的前臂的功能性电刺激（FES）。我们将他的表现与健全的参与者执行相同任务的表现进行了比较，从而为该任务的理想BCI-FES表现确定了量化目标。累积地，这些结果有助于量化与潜在用户及其之间复杂交互相关的BCI解码器性能特征。

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