Chen Weixuan, Liu Xilin, Litt Brian
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2629-32. doi: 10.1109/EMBC.2014.6944162.
One of the most interesting applications of brain computer interfaces (BCIs) is movement prediction. With the development of invasive recording techniques and decoding algorithms in the past ten years, many single neuron-based and electrocorticography (ECoG)-based studies have been able to decode trajectories of limb movements. As the output variables are continuous in these studies, a regression model is commonly used. However, the decoding of limb movements is not a pure regression problem, because the trajectories can be apparently classified into a motion state and a resting state, which result in a binary property overlooked by previous studies. In this paper, we propose an algorithm called logistic-weighted regression to make use of the property, and apply the algorithm to a BCI system decoding flexion of human fingers from ECoG signals. Our results show that the application of logistic-weighted regression improves decoding performance compared to the application of linear regression or pace regression. The proposed algorithm is also immensely valuable in the other BCIs decoding continuous movements.
脑机接口（BCI）最有趣的应用之一是运动预测。在过去十年中，随着侵入性记录技术和解码算法的发展，许多基于单神经元和基于皮层脑电图（ECoG）的研究已经能够解码肢体运动的轨迹。由于这些研究中的输出变量是连续的，因此通常使用回归模型。然而，肢体运动的解码并非纯粹的回归问题，因为轨迹可以明显地分为运动状态和静止状态，这导致了先前研究忽略的二元特性。在本文中，我们提出了一种称为逻辑加权回归的算法来利用这一特性，并将该算法应用于一个从ECoG信号中解码人类手指弯曲的BCI系统。我们的结果表明，与线性回归或逐点回归的应用相比，逻辑加权回归的应用提高了解码性能。所提出的算法在其他解码连续运动的BCI中也具有巨大的价值。
