Evaluación del aporte de la covarianza de las señales electroencefalográficas a las interfaces cerebro-computador de imaginación motora para pacientes con lesiones de médula espinal
Resumen
Las interfaces cerebro-computadora no invasivas basadas en EEG de imaginación motora (miBCI) prometen restaurar efectivamente el control motor a pacientes con discapacidades motoras, por ejemplo, aquellos con lesión de la médula espinal (LME). Sin embargo, todavía es necesario investigar las miBCI, con fines de rehabilitación, para este tipo de pacientes que utilizan dispositivos de adquisición de señales EEG de bajo costo, tales como Emotiv EPOC. En este trabajo, se describe en detalle y se comparan diez arquitecturas miBCI basadas en información de covarianza de señales EEG, adquiridas con Emotiv EPOC, para la decodificación de intención de mano abierta y cerrada en tres sujetos control y dos pacientes con LME cervical. Cuatro de estas diez miBCI usan información de covarianza para construir filtros espaciales y el resto usa la información covarianza como una representación directa de las señales EEG, permitiendo la manipulación directa mediante geometría de Riemann. Como resultado, se encontró que, a pesar de que todas las arquitecturas miBCI tienen una precisión general por encima del nivel de azar, las que utilizan la covarianza como representación directa de las señales EEG junto con clasificadores lineales, superan las miBCI que usan la covarianza para el filtrado espacial, tanto en sujetos de control como en pacientes con LME. Estos resultados sugieren un alto potencial de las miBCI basadas en la geometría de Riemann para la rehabilitación de pacientes con LME, utilizando dispositivos de adquisición de EEG de bajo costo.
Referencias bibliográficas
A. S. Burns et al., “Type and Timing of Rehabilitation Following Acute and Subacute Spinal Cord Injury: A Systematic Review,” Glob. Spine J., vol. 7, no. 3_suppl, p. 175S–194S, Sep. 2017.
https://doi.org/10.1177/2192568217703084
J. van Tuijl, Y. Janssen-Potten, and H. Seelen, “Evaluation of upper extremity motor function tests in tetraplegics,” Spinal Cord, vol. 40, no. 2, pp. 51–64, Feb. 2002. https://doi.org/10.1038/sj.sc.3101261
Paralyzed Veterans of America Consortium for Spinal Cord Medicine, “Preservation of upper limb function following spinal cord injury: a clinical practice guideline for health-care professionals.,” J. Spinal Cord Med., vol. 28, no. 5, pp. 434–470, Sep. 2005. https://doi.org/10.1080/10790268.2005.11753844
R. Rupp, S. C. Kleih, R. Leeb, J. del R. Millan, A. Kübler, and G. R. Müller-Putz, “Brain–Computer Interfaces and Assistive Technology,” in Brain-Computer-Interfaces in their ethical, social and cultural contexts, vol. 12, Dordrecht: Springer Netherlands, 2014, pp. 7–38. https://doi.org/10.1007/978-94-017-8996-7_2
G. Dornhege, J. del R. Millán, T. Hinterberger, D. J. McFarland, and K.-R. Müller, Toward Brain-Computer Interfacing, Cambridge: MIT Press, 2007.
J. J. Shih, D. J. Krusienski, and J. R. Wolpaw, “Brain-Computer Interfaces in Medicine,” Mayo Clin. Proc., vol. 87, no. 3, pp. 268–279, Mar. 2012. https://doi.org/10.1016/j.mayocp.2011.12.008
C. D. Ferrin Bolaños and H. Loaiza Correa, “Interfaz cerebro-computador multimodal para procesos de neurorrehabilitación de miembros superiores en pacientes con lesiones de médula espinal: una revisión,” Rev. Ing. Biomédica, vol. 12, no. 24, pp. 35–46, Dec. 2018. https://doi.org/10.24050/19099762.n24.2018.1222
L. M. Alonso-Valerdi, R. A. Salido-Ruiz, and R. A. Ramirez-Mendoza, “Motor imagery based brain–computer interfaces: An emerging technology to rehabilitate motor deficits,” Neuropsychologia, vol. 79 part B., pp. 354–363, Dec. 2015. https://doi.org/10.1016/j.neuropsychologia.2015.09.012
P. H. Peckham et al., “Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: A multicenter study,” Arch. Phys. Med. Rehabil., vol. 82, no. 10, pp. 1380–1388, Oct. 2001.
https://doi.org/10.1053/apmr.2001.25910
S. Mateo, F. Di Rienzo, V. Bergeron, A. Guillot, C. Collet, and G. Rode, “Motor imagery reinforces brain compensation of reach-to-grasp movement after cervical spinal cord injury,” Front. Behav. Neurosci., vol. 9, pp. 1–12, Sep. 2015. https://doi.org/10.3389/fnbeh.2015.00234
M. Grangeon, P. Revol, A. Guillot, G. Rode, and C. Collet, “Could motor imagery be effective in upper limb rehabilitation of individuals with spinal cord injury? A case study,” Spinal Cord, vol. 50, no. 10, pp. 766–771, Oct. 2012. https://doi.org/10.1038/sc.2012.41
C. Vidaurre, C. Klauer, T. Schauer, A. Ramos-Murguialday, and K.-R. Müller, “EEG-based BCI for the linear control of an upper-limb neuroprosthesis,” Med. Eng. Phys., vol. 38, no. 11, pp. 1195–1204, Nov. 2016.
https://doi.org/10.1016/j.medengphy.2016.06.010
J. Castillo, “Interfaz Cerebro-Computador Adaptativa Basada en Agentes Software para la Discriminación de Cuatro Tareas Mentales”, Tesis Doctoral, Facultad de Ingeniería, Universidad del Valle, Cali, 2015. [En línea] Disponible en: http://hdl.handle.net/10893/10290
A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Classification of covariance matrices using a Riemannian-based kernel for BCI applications,” Neurocomputing, vol. 112, pp. 172–178, Jul. 2013.
https://doi.org/10.1016/j.neucom.2012.12.039
J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 3rd ed. New Jersey: Prentice-Hall, Inc., 1996.
C. Bishop, Pattern Recognition and Machine Learning, 1st ed. New York: Springer-Verlag New York, 2006.
C. M. Bishop, Neural networks for pattern recognition. New York: Oxford University Press, 1995.
E. Giraldo-Suárez, J. I. Padilla-Buriticá, and C. G. Castellanos-Domínguez, “Dynamic inverse problem solution using a kalman filter smoother for neuronal activity estimation,” TecnoLógicas, no. 27, p. 33-51, Dec. 2011.
https://doi.org/10.22430/22565337.3
C. G. Lemus, “Análisis de reducción de ruido en señales eeg orientado al reconocimiento de patrones,” Tecnológicas, no. 21, pp. 67–80, Dec. 2008. https://doi.org/10.22430/22565337.248
S. Mingxu, “A functional electrical stimulation (fes) control system for upper limb rehabilitation”, PhD Thesis, School of Computing, Science and Engineering, University of Salford, Salford, 2014.
[En línea] Disponible en: http://usir.salford.ac.uk/id/eprint/32854
A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Multiclass brain-computer interface classification by Riemannian geometry,” IEEE Trans. Biomed. Eng., vol. 59, no. 4, pp. 920–928, Apr. 2012.
https://doi.org/10.1109/TBME.2011.2172210
M. Tangermann et al., “Review of the BCI Competition IV,” Front. Neurosci., vol. 6, no. 55, pp. 1–31, July. 2012.
https://doi.org/10.3389/fnins.2012.00055
B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. Muller, “Optimizing Spatial filters for Robust EEG Single-Trial Analysis,” IEEE Signal Process. Mag., vol. 25, no. 1, pp. 41–56, Dec. 2008.
https://doi.org/10.1109/MSP.2008.4408441
F. Lotte and C. Guan, “Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms,” IEEE Trans. Biomed. Eng., vol. 58, no. 2, pp. 355–362, Feb. 2011. https://doi.org/10.1109/TBME.2010.2082539
S. Ge, R. Wang, and D. Yu, “Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography,” PLoS One, vol. 9, no. 6, p. e98019, Jun. 2014.
https://doi.org/10.1371/journal.pone.0098019
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York: Springer, 2017. https://doi.org/10.1007/b94608
S. Haykin, Neural Networks and Learning Machines, 3rd ed. Ontario: Pearson, Prentice Hall, 2008.
A. Barachant, “Commande robuste d’un effecteur par une interface cerveau-machine EEG asynchrone”, Tesis Doctoral, Electronique et système pour la santè, Université de Grenoble, Grenoble, 2006.
[En línea] Disponible en: https://tel.archives-ouvertes.fr/tel-01196752
A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Riemannian Geometry Applied to BCI Classification,” in 9th International Conference Latent Variable Analysis and Signal Separation, France: Springer, 2010, pp. 629-636.
https://doi.org/10.1007/978-3-642-15995-4_78
M. Congedo, A. Barachant, and A. Andreev, “A New Generation of Brain-Computer Interface Based on Riemannian Geometry,” arXiv Prepr., p. 33, Oct. 2013.
F. Yger, M. Berar, and F. Lotte, “Riemannian Approaches in Brain-Computer Interfaces: A Review,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 10, pp. 1753–1762, Oct. 2017.
https://doi.org/10.1109/TNSRE.2016.2627016
K. Holewa and A. Nawrocka, “Emotiv EPOC neuroheadset in brain - computer interface,” in Proceedings of the 2014 15th International Carpathian Control Conference (ICCC), Velke Karlovice, 2014, pp. 149–152.
https://doi.org/10.1109/CarpathianCC.2014.6843587
J.-A. Martinez-Leon, J.-M. Cano-Izquierdo, and J. Ibarrola, “Are low cost Brain Computer Interface headsets ready for motor imagery applications?,” Expert Syst. Appl., vol. 49, pp. 136–144, May. 2016.
https://doi.org/10.1016/j.eswa.2015.11.015
N. A. Badcock et al., “Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children,” PeerJ, vol. 3, p. 17, Apr. 2015. https://doi.org/10.7717/peerj.907
J. Lu, D. J. McFarland, and J. R. Wolpaw, “Adaptive Laplacian filtering for sensorimotor rhythm-based brain–computer interfaces,” J. Neural Eng., vol. 10, no. 1, p. 016002, Dec. 2012.
https://doi.org/10.1088/1741-2560/10/1/016002
F. Lotte, “A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain–Computer Interfaces,” in Guide to Brain-Computer Music Interfacing, London: Springer London, 2014, pp. 133–161.
https://doi.org/10.1007/978-1-4471-6584-2_7
D. Wu, J.-T. King, C.-H. Chuang, C.-T. Lin, and T.-P. Jung, “Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI),” IEEE Trans. Fuzzy Syst., vol. 26, no. 2, pp. 771–781, Apr. 2018.
https://doi.org/10.1109/TFUZZ.2017.2688423
S. Yu, L.-C. Tranchevent, B. De Moor, and Y. Moreau, “Rayleigh Quotient-Type Problems in Machine Learning,” in Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining, Heidelberg: Springer, 2011, pp. 27-37. https://doi.org/10.1007/978-3-642-19406-1_2
D. P. Bertsekas, Nonlinear Programming, 3rd ed. Massachussets: Athena Scientific, 2016.
D. Watkins, “6. The Generalized Eigenvalue Problem,” in The Matrix Eigenvalue Problem, 1st ed., Washington: Society for Industrial and Applied Mathematics, 2007, pp. 233–263.
https://doi.org/10.1137/1.9780898717808.ch6
T. Fushiki, “Estimation of prediction error by using K-fold cross-validation,” Stat. Comput., vol. 21, no. 2, pp. 137–146, Apr. 2011. https://doi.org/10.1007/s11222-009-9153-8
E. Combrisson and K. Jerbi, “Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy,” J. Neurosci. Methods, vol. 250, pp. 126–136, Jul. 2015. https://doi.org/10.1016/j.jneumeth.2015.01.010
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, Jun. 2006.
https://doi.org/10.1016/j.patrec.2005.10.010
A. Barachant, S. Bon, M. Congedo, and C. Jutten, “Common Spatial Pattern revisited by Riemannian geometry,” in 2010 IEEE International Workshop on Multimedia Signal Processing, Saint Malo, 2010, pp. 472–476.
https://doi.org/10.1109/MMSP.2010.5662067
K. A. Moxon, A. Oliviero, J. Aguilar, and G. Foffani, “Cortical reorganization after spinal cord injury: Always for good?,” Neuroscience, vol. 283, pp. 78–94, Dec. 2014. https://doi.org/10.1016/j.neuroscience.2014.06.056
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