IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0061976.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061976
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0061976&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0061976?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Josh Merel & David Carlson & Liam Paninski & John P Cunningham, 2016. "Neuroprosthetic Decoder Training as Imitation Learning," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-24, May.
    2. Andrey Eliseyev & Tetiana Aksenova, 2016. "Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    3. Ujwal Chaudhary & Bin Xia & Stefano Silvoni & Leonardo G Cohen & Niels Birbaumer, 2017. "Brain–Computer Interface–Based Communication in the Completely Locked-In State," PLOS Biology, Public Library of Science, vol. 15(1), pages 1-25, January.
    4. Nuri F Ince & Rahul Gupta & Sami Arica & Ahmed H Tewfik & James Ashe & Giuseppe Pellizzer, 2010. "High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-11, December.
    5. Keundong Lee & Angelique C. Paulk & Yun Goo Ro & Daniel R. Cleary & Karen J. Tonsfeldt & Yoav Kfir & John S. Pezaris & Youngbin Tchoe & Jihwan Lee & Andrew M. Bourhis & Ritwik Vatsyayan & Joel R. Mart, 2024. "Flexible, scalable, high channel count stereo-electrode for recording in the human brain," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Jose M Carmena, 2013. "Advances in Neuroprosthetic Learning and Control," PLOS Biology, Public Library of Science, vol. 11(5), pages 1-4, May.
    7. Shinsuke Koyama & Uri Eden & Emery Brown & Robert Kass, 2010. "Bayesian decoding of neural spike trains," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 37-59, February.
    8. Baoguo Xu & Wenlong Li & Deping Liu & Kun Zhang & Minmin Miao & Guozheng Xu & Aiguo Song, 2022. "Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    9. Yasuhiko Nakanishi & Takufumi Yanagisawa & Duk Shin & Ryohei Fukuma & Chao Chen & Hiroyuki Kambara & Natsue Yoshimura & Masayuki Hirata & Toshiki Yoshimine & Yasuharu Koike, 2013. "Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    10. Xiao-yu Sun & Bin Ye, 2023. "The functional differentiation of brain–computer interfaces (BCIs) and its ethical implications," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    11. Zheng Li & Joseph E O'Doherty & Timothy L Hanson & Mikhail A Lebedev & Craig S Henriquez & Miguel A L Nicolelis, 2009. "Unscented Kalman Filter for Brain-Machine Interfaces," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-18, July.
    12. Sean L. Metzger & Jessie R. Liu & David A. Moses & Maximilian E. Dougherty & Margaret P. Seaton & Kaylo T. Littlejohn & Josh Chartier & Gopala K. Anumanchipalli & Adelyn Tu-Chan & Karunesh Ganguly & E, 2022. "Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. David Balderas & Pedro Ponce & Diego Lopez-Bernal & Arturo Molina, 2021. "Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces," Future Internet, MDPI, vol. 13(8), pages 1-27, August.
    14. Eric A Pohlmeyer & Babak Mahmoudi & Shijia Geng & Noeline W Prins & Justin C Sanchez, 2014. "Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
    15. Pavlov, A.N. & Grishina, D.S. & Runnova, A.E. & Maksimenko, V.A. & Pavlova, O.N. & Shchukovsky, N.V. & Hramov, A.E. & Kurths, J., 2019. "Recognition of electroencephalographic patterns related to human movements or mental intentions with multiresolution analysis," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 230-235.
    16. Betz, Ulrich A.K. & Arora, Loukik & Assal, Reem A. & Azevedo, Hatylas & Baldwin, Jeremy & Becker, Michael S. & Bostock, Stefan & Cheng, Vinton & Egle, Tobias & Ferrari, Nicola & Schneider-Futschik, El, 2023. "Game changers in science and technology - now and beyond," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. 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.
    18. Fuji Ren & Yanwei Bao, 2020. "A Review on Human-Computer Interaction and Intelligent Robots," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 5-47, February.
    19. Han-Lin Hsieh & Maryam M Shanechi, 2018. "Optimizing the learning rate for adaptive estimation of neural encoding models," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-34, May.
    20. Elisa Donati & Giacomo Valle, 2024. "Neuromorphic hardware for somatosensory neuroprostheses," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0061976. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.