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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