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.Comment: 6 pages, 3 figures, 4 tables. Preprint. Under revie