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.
@article{arxiv.2211.01960,
title = {FingerFlex: Inferring Finger Trajectories from ECoG signals},
author = {Vladislav Lomtev and Alexander Kovalev and Alexey Timchenko},
journal= {arXiv preprint arXiv:2211.01960},
year = {2023}
}
Comments
6 pages, 3 figures, 4 tables. Preprint. Under review