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A cryptography-based approach for movement decoding

View ORCID ProfileEva L. Dyer, Mohammad Gheshlaghi Azar, Hugo L. Fernandes, Matthew G. Perich, Stephanie Naufel, Lee Miller, Konrad P. Körding
doi: https://doi.org/10.1101/080861
Eva L. Dyer
1Dept. of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
2Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL
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  • ORCID record for Eva L. Dyer
  • For correspondence: edyer@ric.org
Mohammad Gheshlaghi Azar
1Dept. of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
2Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL
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Hugo L. Fernandes
1Dept. of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
2Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL
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Matthew G. Perich
3Dept. of Biomedical Engineering, Northwestern University, Evanston, IL
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Stephanie Naufel
3Dept. of Biomedical Engineering, Northwestern University, Evanston, IL
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Lee Miller
1Dept. of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
3Dept. of Biomedical Engineering, Northwestern University, Evanston, IL
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Konrad P. Körding
1Dept. of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
2Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL
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Abstract

Brain decoders use neural recordings to infer a user’s activity or intent. To train a decoder, we generally need infer the variables of interest (covariates) using simultaneously measured neural activity. However, there are many cases where this approach is not possible. Here we overcome this problem by introducing a fundamentally new approach for decoding called distribution alignment decoding (DAD). We use the statistics of movement, much like cryptographers use the statistics of language, to find a mapping between neural activity and motor variables. DAD learns a linear decoder which aligns the distribution of its output with the typical distribution of motor outputs by minimizing their KL-divergence. We apply our approach to a two datasets collected from the motor cortex of non-human primates (NHPs): a reaching task and an isometric force production task. We study the performance of DAD and find regimes where DAD provides comparable and in some cases, better performance than a typical supervised decoder. As DAD does not rely on the ability to record motor-related outputs, it promises to broaden the set of potential applications of brain decoding.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 14, 2016.
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A cryptography-based approach for movement decoding
Eva L. Dyer, Mohammad Gheshlaghi Azar, Hugo L. Fernandes, Matthew G. Perich, Stephanie Naufel, Lee Miller, Konrad P. Körding
bioRxiv 080861; doi: https://doi.org/10.1101/080861
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A cryptography-based approach for movement decoding
Eva L. Dyer, Mohammad Gheshlaghi Azar, Hugo L. Fernandes, Matthew G. Perich, Stephanie Naufel, Lee Miller, Konrad P. Körding
bioRxiv 080861; doi: https://doi.org/10.1101/080861

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