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On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals

Author

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  • Javier M Antelis
  • Luis Montesano
  • Ander Ramos-Murguialday
  • Niels Birbaumer
  • Javier Minguez

Abstract

Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0061976
    DOI: 10.1371/journal.pone.0061976
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    1. 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.
    2. Mijail D. Serruya & Nicholas G. Hatsopoulos & Liam Paninski & Matthew R. Fellows & John P. Donoghue, 2002. "Instant neural control of a movement signal," Nature, Nature, vol. 416(6877), pages 141-142, March.
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    1. 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.

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