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Vladislav Lomtev · Alexander Kovalev · Alex Timchenko
Paper | Project Page | Some text
We propose FingerFlex, a new state of the art model for prediction finger movements from brain activity(ECoG).
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.
Model architecture
We test our FingerFlex on multiple datasets BCI Competition IV and Stanfore which covers various subjects and different ECoG positions.
Example.mp4
@article{fingerflex2022,
title={FingerFlex: Inferring Finger Trajectories from ECoG signals},
author={Lomtev, Vladislav and Kovalev, Alexander and Timchenko, Alexey},
journal={arXiv preprint arXiv:2211.01960},
year={2022}
}