A neural network for online spike classification that improves decoding accuracy
A neural network for online spike classification that improves decoding accuracy
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DOI：
10.1152/jn.00641.2019
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发表时间：
2020-04-01
影响因子：
2.5
通讯作者：
Smith, Matthew A.
中科院分区：
文献类型：
--
作者：
Issar, Deepa;Williamson, Ryan C.;Smith, Matthew A.
从噪声中分离神经信号可以提高脑机接口的性能和稳定性。然而，大多数用于从噪声中分离神经动作电位的算法不适合于在真实的时间中使用，并且已经显示出对解码性能的混合效果。为了消除阻碍在线解码的噪声，我们试图使人类尖峰分类器的直觉自动化，以真实的时间进行操作，并使用易于调节的参数来控制尖峰波形分类的严格性。我们训练了一个人工神经网络，其中一个隐藏层是人工标记为尖峰或噪声的神经波形。网络输出是其分类的每个波形的似然度量，我们通过改变波形的最小似然值来调整网络的严格性，以将其视为尖峰。使用网络的标签，以排除噪声波形，我们解码记忆引导扫视任务期间，从电极阵列植入在前额叶皮层的恒河猴记住的目标位置。该网络对波形进行真实的分类，其分类在性质上类似于人类尖峰分类器的分类。与阈值交叉解码相比，在大多数会话中，我们通过去除具有低尖峰似然值的波形来提高解码性能。此外，随着阵列植入时间的增加，使用我们的网络分类进行解码变得更加有益。我们的分类器作为一个可行的预处理步骤，几乎没有伤害的风险，可以应用于离线神经数据分析和在线解码。新&值得注意的是虽然有许多尖峰排序方法，隔离定义良好的单个单元，这些方法通常涉及人为干预，并有不一致的解码效果。我们使用人类分类的神经波形作为训练数据来创建一个人工神经网络，该网络可以被调整以将尖峰信号与损害解码的噪声分开。我们发现，该网络运行在真实的时间，是适合离线数据处理和在线解码。
Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network's stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network's labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our network's classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding.NEW & NOTEWORTHY Although there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both off-line data processing and online decoding.