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Using HMM to Model Neural Dynamics and Decode Useful Signals for Neuroprosthetic Control

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Hidden Markov Models and Applications

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

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Abstract

A central aim of modern neuroscience is to unravel how the various brain areas coordinate their activity across time and how they control behaviour. In this chapter, we first provide the rationale for the application of mathematical methods that analyse neural signals with a population approach. Among these techniques, Hidden Markov Models (HMMs) have been extensively applied. We then presented the key concepts of HMMs and the main problems during their application. Using neural data recorded from three distinct sectors in the monkey parietal cortex during an arm movement task, we show how HMMs can be used both to define neural dynamics and the flow of information at the network level, but also to decode relevant task parameters from neural activity in order to control finite state brain–machine interfaces (BMIs). We found that the HMM state transitions were in accordance with a functional gradient in the parietal cortex and that they carry reliable information about target position and behavioural phases. Finally, we provide a brief review of the literature on HMMs in the field and an overview of other approaches that can model time series.

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Author notes

  1. Diomedi Stefano and Vaccari Francesco Edoardo contributed equally with all other contributors.

Authors and Affiliations

  1. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy

    Stefano Diomedi, Francesco Edoardo Vaccari, Kostas Hadjidimitrakis & Patrizia Fattori

  2. Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy

    Patrizia Fattori

Authors
  1. Stefano Diomedi
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  2. Francesco Edoardo Vaccari
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  3. Kostas Hadjidimitrakis
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  4. Patrizia Fattori
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Corresponding author

Correspondence to Kostas Hadjidimitrakis .

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Editors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

  2. Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Wentao Fan

  3. Grenoble Institute of Technology, Grenoble, France

    Manar Amayri

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Diomedi, S., Vaccari, F.E., Hadjidimitrakis, K., Fattori, P. (2022). Using HMM to Model Neural Dynamics and Decode Useful Signals for Neuroprosthetic Control. In: Bouguila, N., Fan, W., Amayri, M. (eds) Hidden Markov Models and Applications. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-99142-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-99142-5_3

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