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

  • Martin SpülerEmail author
  • Florian Grimm
  • Alireza Gharabaghi
  • Martin Bogdan
  • Wolfgang Rosenstiel
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 12)


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.


Independent Component Analysis Support Vector Regression Canonical Correlation Analysis Movement Trajectory Compensatory Movement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Martin Spüler
    • 1
    Email author
  • Florian Grimm
    • 2
  • Alireza Gharabaghi
    • 2
  • Martin Bogdan
    • 3
  • Wolfgang Rosenstiel
    • 1
  1. 1.Department of Computer Engineering, Wilhelm Schickard Institute for Computer ScienceUniversity of TübingenTübingenGermany
  2. 2.Functional and Restorative Neurosurgery Unit, Department of NeurosurgeryUniversity Clinic TübingenTübingenGermany
  3. 3.Department of Computer EngineeringUniversity of LeipzigLeipzigGermany

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