论文标题
FingerFlex：从ECOG信号推断手指轨迹
FingerFlex: Inferring Finger Trajectories from ECoG signals
论文作者
论文摘要
运动脑计算机界面（BCI）的开发非常依赖于神经时间序列解码算法。深度学习体系结构的最新进展允许自动选择数据中的高阶依赖性。本文介绍了FingerFlex模型 - 一种卷积编码器架构，适用于电视图（ECOG）大脑数据的指导运动回归。在公开可用的BCI竞争IV数据集4上实现了最先进的性能，其相关系数在真实和预测轨迹之间的相关系数高达0.74。提出的方法为开发全功能的高精度皮层运动脑部计算机界面提供了机会。
Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-order dependencies in data. This article presents the FingerFlex model - a convolutional encoder-decoder architecture adapted for finger movement regression on electrocorticographic (ECoG) brain data. State-of-the-art performance was achieved on a publicly available BCI competition IV dataset 4 with a correlation coefficient between true and predicted trajectories up to 0.74. The presented method provides the opportunity for developing fully-functional high-precision cortical motor brain-computer interfaces.
