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Effect of Hand Dominance When Decoding Motor Imagery Grasping Tasks

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Computational Neuroscience (LAWCN 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1519))

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Abstract

Discriminating between different motor imagery tasks within the same limb is challenging because of the proximity of their spatial representations on the motor cortex. Overcoming this challenge would largely increase the number of control dimensions and pave the way for more practical brain-computer interfaces (BCIs). This paper explores how hand dominance affects classification performance when decoding different motor imagery tasks completed with the same hand. This aspect has, to the best of our knowledge, not been presented in the literature before. The performance was also evaluated to see if acceptable accuracies for real-life applications could be reached without handcrafted features. EEG signals were collected from nine subjects performing the same set of imagery grasping tasks with both dominant and non-dominant hand. The signals were analyzed using traditional state-of-the-art methods, such as filterbank common spatial patterns (FBCSP) and Tangent Space (TS), in addition to well-validated convolutional neural networks designed for limited data. Automatic channel selection according to a Riemannian geometry criterion before classification improved discrimination. Variation in performance when using dominant versus non-dominant hand was found for all the subjects. To establish whether these differences are statistically significant, or to identify trends, more data is required. Five out of nine subjects achieved accuracies above 70% for classification within the same hand without using tailored features.

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Acknowledgments

This work is partially supported by The Research Council of Norway as a part of the Predictive and Intuitive Robot Companion (PIRC) project under grant agreement 312333 and through its Centres of Excellence scheme, RITMO with project No. 262762. The authors would also like to thank Rahul Omprakash Agrawal and Henrik Eijsink for assistance with the lab and useful discussions.

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Authors and Affiliations

  1. Department of Informatics, University of Oslo, Oslo, Norway

    Katrine Linnea Nergård & Jim Torresen

  2. Department of Psychology, University of Oslo, Oslo, Norway

    Tor Endestad

  3. RITMO, University of Oslo, Oslo, Norway

    Tor Endestad & Jim Torresen

Authors
  1. Katrine Linnea Nergård
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  2. Tor Endestad
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  3. Jim Torresen
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Corresponding author

Correspondence to Katrine Linnea Nergård .

Editor information

Editors and Affiliations

  1. Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil

    Paulo Rogério de Almeida Ribeiro

  2. Federal University of São João del-Rei, São João del-Rei, Minas Gerais, Brazil

    Vinícius Rosa Cota

  3. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Dante Augusto Couto Barone

  4. Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil

    Alexandre César Muniz de Oliveira

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Nergård, K.L., Endestad, T., Torresen, J. (2022). Effect of Hand Dominance When Decoding Motor Imagery Grasping Tasks. In: Ribeiro, P.R.d.A., Cota, V.R., Barone, D.A.C., de Oliveira, A.C.M. (eds) Computational Neuroscience. LAWCN 2021. Communications in Computer and Information Science, vol 1519. Springer, Cham. https://doi.org/10.1007/978-3-031-08443-0_15

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  • DOI: https://doi.org/10.1007/978-3-031-08443-0_15

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-08443-0

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