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Assessing Movement Factors in Upper Limb Kinematics Decoding from EEG Signals

Author

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  • Andrés Úbeda
  • Enrique Hortal
  • Eduardo Iáñez
  • Carlos Perez-Vidal
  • Jose M Azorín

Abstract

The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer’s hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.

Suggested Citation

  • Andrés Úbeda & Enrique Hortal & Eduardo Iáñez & Carlos Perez-Vidal & Jose M Azorín, 2015. "Assessing Movement Factors in Upper Limb Kinematics Decoding from EEG Signals," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-12, May.
    • Handle: RePEc:plo:pone00:0128456
    DOI: 10.1371/journal.pone.0128456
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    References listed on IDEAS

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    1. Miguel A. L. Nicolelis, 2001. "Actions from thoughts," Nature, Nature, vol. 409(6818), pages 403-407, January.
    2. Leigh R. Hochberg & Mijail D. Serruya & Gerhard M. Friehs & Jon A. Mukand & Maryam Saleh & Abraham H. Caplan & Almut Branner & David Chen & Richard D. Penn & John P. Donoghue, 2006. "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, Nature, vol. 442(7099), pages 164-171, July.
    3. Leigh R. Hochberg & Daniel Bacher & Beata Jarosiewicz & Nicolas Y. Masse & John D. Simeral & Joern Vogel & Sami Haddadin & Jie Liu & Sydney S. Cash & Patrick van der Smagt & John P. Donoghue, 2012. "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm," Nature, Nature, vol. 485(7398), pages 372-375, May.
    4. Meel Velliste & Sagi Perel & M. Chance Spalding & Andrew S. Whitford & Andrew B. Schwartz, 2008. "Cortical control of a prosthetic arm for self-feeding," Nature, Nature, vol. 453(7198), pages 1098-1101, June.
    5. Javier M Antelis & Luis Montesano & Ander Ramos-Murguialday & Niels Birbaumer & Javier Minguez, 2013. "On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-14, April.
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