Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network

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Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network
Title:
Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network
Journal Title:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Keywords:
Publication Date:
08 September 2022
Citation:
Premchand, B., Toe, K. K., Wang, C., Libedinsky, C., Ang, K. K., & So, R. Q. (2022). Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc48229.2022.9870896
Abstract:
Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well know n how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - BMRC-EDB IAF and JCO-DP grant
Grant Reference no. : 1021710167
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2694-0604
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