Data-efficient Causal Decoding of Spiking Neural Activity using Weighted Voting.
Data-efficient Causal Decoding of Spiking Neural Activity using Weighted Voting.
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使用加权投票对尖峰神经活动进行数据高效的因果解码。
DOI：
10.1109/embc46164.2021.9631022
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Valero-Cuevas,FranciscoJ
中科院分区：
文献类型：
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作者：
Marjaninejad,Ali;Klaes,Christian;Valero-Cuevas,FranciscoJ
脑机接口系统可以为大量的应用做出贡献，例如克服神经损伤患者的身体残疾或健康个体的免提设备控制。然而，拥有能够在线准确解释意图的系统仍然是该领域的挑战。鲁棒和数据高效解码-尽管皮质活动的动态性质和物理功能的因果关系要求-是限制这些设备在现实世界应用中广泛使用的最重要的挑战之一。在这里，我们提出了一个因果关系，数据高效的神经解码管道，通过首先在短滑动窗口中对记录进行分类来预测意图。接下来，它对直到当前时间点的初始预测执行加权投票，以报告改进的最终预测。我们证明了它的效用分类尖峰神经活动收集从人类后顶叶皮层的线索，延迟，想象的运动任务。该流水线提供了比最先进的基于时间窗尖峰活动的因果方法更高的分类精度，并且对超参数的选择是鲁棒的。
Brain-Computer Interface systems can contribute to a vast set of applications such as overcoming physical disabilities in people with neural injuries or hands-free control of devices in healthy individuals. However, having systems that can accurately interpret intention online remains a challenge in this field. Robust and data-efficient decoding—despite the dynamical nature of cortical activity and causality requirements for physical function—is among the most important challenges that limit the widespread use of these devices for real-world applications. Here, we present a causal, data-efficient neural decoding pipeline that predicts intention by first classifying recordings in short sliding windows. Next, it performs weighted voting over initial predictions up to the current point in time to report a refined final prediction. We demonstrate its utility by classifying spiking neural activity collected from the human posterior parietal cortex for a cue, delay, imaginary motor task. This pipeline provides higher classification accuracy than state-of-the-art time windowed spiking activity based causal methods, and is robust to the choice of hyper-parameters.