IEEE Trans Neural Syst Rehabil Eng. 2018 Dec;26(12):2240-2248. doi: 10.1109/TNSRE.2018.2875731. Epub 2018 Oct 12.
In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson's correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.
在本文中，我们研究了单指和多指运动之间的关系。通过利用运动之间时间发放模式的神经相关性，我们表明，物理相关运动对的 Pearson 相关系数大于其他运动对的 Pearson 相关系数；当指令执行相应的多指运动时，对单指运动调谐的神经元的发放率也会增加。我们还使用层次聚类分析不仅验证了单指和多指运动之间的关系，还验证了弯曲和伸展运动之间的关系。此外，我们提出了一种新的解码方法，该方法在省略多指运动训练过程的情况下对神经发放模式进行建模。对于解码，使用 Skellam 和高斯概率分布作为数学模型。使用在单指运动过程中获得的神经活动来估计多指运动的概率分布模型。结果表明，当使用所有神经元进行多指运动时，所提出的神经解码精度与有监督的神经解码精度相当。这些结果表明，只有单指运动的神经活动可用于灵巧的多指神经假肢控制。
