FingerFlex: Inferring Finger Trajectories from ECoG signals
- URL: http://arxiv.org/abs/2211.01960v2
- Date: Tue, 25 Apr 2023 19:14:18 GMT
- Title: FingerFlex: Inferring Finger Trajectories from ECoG signals
- Authors: Vladislav Lomtev, Alexander Kovalev, Alexey Timchenko
- Abstract summary: FingerFlex model is 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.
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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