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Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients

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Advances in Neurotechnology, Electronics and Informatics

Part of the book series: Biosystems & Biorobotics ((BIOSYSROB,volume 12))

Abstract

Decoding the neural activity based on ECoG signals is widely used in the field of Brain-Computer Interfaces (BCIs) to predict movement trajectories or control a prosthetic device. However, there are only few reports of using ECoG in stroke patients. In this paper, we compare different methods for predicting contralateral movement trajectories from epidural ECoG signals recorded over the lesioned hemisphere in three chronic stroke patients. The results show that movement trajectories can be predicted with correlation coefficients ranging from 0.24 to 0.64. Depending on the intended application, either the use of Support Vector Regression (SVR) or Canonical Correlation Analysis (CCA) obtained the best results. By investigating how ECoG based decoding performs in comparison with EMG based decoding it becomes visible that abnormal muscle activation patterns affect the prediction and that using activity of only the forearm muscles, there is no significant difference between ECoG and EMG for predicting wrist movement trajectory.

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Acknowledgments

This manuscript is an extended version of a paper presented at the 2nd International Congress on Neurotechnology, Electronics and Informatics [34]. The work was supported by the European Research Council (ERC 227632-BCCI) and the Baden-Württemberg Stiftung (GRUENS).

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Authors and Affiliations

  1. Department of Computer Engineering, Wilhelm Schickard Institute for Computer Science, University of Tübingen, Sand 13, 72076, Tübingen, Germany

    Martin Spüler & Wolfgang Rosenstiel

  2. Functional and Restorative Neurosurgery Unit, Department of Neurosurgery, University Clinic Tübingen, Tübingen, Germany

    Florian Grimm & Alireza Gharabaghi

  3. Department of Computer Engineering, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany

    Martin Bogdan

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  1. Martin Spüler
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  3. Alireza Gharabaghi
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  4. Martin Bogdan
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  5. Wolfgang Rosenstiel
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Correspondence to Martin Spüler .

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Editors and Affiliations

  1. Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal

    Ana Rita Londral

  2. Faculty of Engineering, Universidade Católica Portuguesa, Lisbon, Portugal

    Pedro Encarnação

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Spüler, M., Grimm, F., Gharabaghi, A., Bogdan, M., Rosenstiel, W. (2016). Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients. In: Londral, A., Encarnação, P. (eds) Advances in Neurotechnology, Electronics and Informatics. Biosystems & Biorobotics, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-26242-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-26242-0_9

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