Regístrese
Buscar en
Revista Iberoamericana de Automática e Informática Industrial RIAI
Toda la web
Inicio Revista Iberoamericana de Automática e Informática Industrial RIAI Nuevo Enfoque para la Clasificación de Señales EEG usando la Varianza de la Di...
Información de la revista
Vol. 14. Núm. 4.
Páginas 362-371 (Octubre - Diciembre 2017)
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Visitas
1064
Vol. 14. Núm. 4.
Páginas 362-371 (Octubre - Diciembre 2017)
DOI: 10.1016/j.riai.2017.07.002
Open Access
Nuevo Enfoque para la Clasificación de Señales EEG usando la Varianza de la Diferencia entre las Clases de un Clasificador Bayesiano
New Approach to the EEG Signals Classification using the Variance of the Difference between the Classes of a Bayesian Classifier
Visitas
1064
Thomaz R. Botelhoa,b,
Autor para correspondencia
thomazrb@ifes.edu.br

Autor para correspondencia.
, Douglas Sopranib, Camila Rodriguesa, André Ferreiraa, Anselmo Frizeraa
a Programa de Posgrado en Ingeniería Eléctrica, Universidad Federal de Espírito Santo (UFES), Av. Fernando Ferrari, 514, Vitória-ES, Brasil
b Departamento de Electrotecnología, Instituto Federal de Educación, Ciencia y Tecnología de Espírito Santo (IFES), BR 101 Av. Norte, 58, São Mateus -ES, Brasil
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas
Resumen

Los avances en robótica de rehabilitación están beneficiando en gran medida a los pacientes con discapacidad física. Los dispositivos de asistencia y rehabilitación pueden basar su funcionamiento en información fisiológica de los músculos y del cerebro a través de electromiografía (EMG) y electroencefalografía (EEG), para detectar la intención de movimiento de los usuarios. En este trabajo se presenta una propuesta de interfaz multimodal para la adquisición, sincronización y procesamiento de señales EEG y de sensores inerciales, para ser aplicada en tareas de rehabilitación con exoesqueletos robóticos. Se realizaron experimentos con individuos sanos con el objetivo de analizar la intención de movimiento, la activación muscular e inicio de movimiento durante los movimientos de extensión de la rodilla. Esta propuesta es un nuevo enfoque para la clasificación de señales EEG usando un clasificador bayesiano tomando en cuenta la varianza de la diferencia entre las clases usadas. El aporte de este trabajo se sustenta con los resultados que muestran un incremento del 30% en la precisión de clasificación con señales EEG en comparación con los enfoques tradicionales de clasificación, en un análisis off-line para el reconocimiento de la intención de movimiento de los miembros inferiores.

Palabras clave:
Interfaz hombre-máquina
Análisis de señales
Sistemas biomédicos
Unidades de medición inercial
Cerebro humano
Movimiento
Abstract

Patients with physical disabilities can benefit from robotic rehabilitation. This improves the efficiency of recovery and, therefore, the rehabilitation of the patient. Assistive and rehabilitation devices can make use of physiological data, such as electromyography (EMG) and electroencephalography (EEG), in order to detect movement intentions. This work presents a multimodal interface for signal acquisition, synchronization and processing of EEG and inertial sensors signals, to be applied in rehabilitation robotic exoskeletons. Experiments were performed with healthy individuals executing knee extension. The goal is to analyze movement intention, muscle activation and movement onset. It was proposed a new approach to the EEG signals classification using a Bayesian classifier taking into account the variance of the difference between the classes used. This contribution presents an average improvement of about 30% in the EEG classification accuracy in comparison to the traditional classifier approach. In this work an offline analysis was conducted.

Keywords:
Human-machine interface
Signal analysis
Biomedical systems
Inertial measurement units
Human brain
Movement
Referencias
[Ada et al., 2010]
L. Ada, C.M. Dean, J. Vargas, S. Ennis.
Mechanically assisted walking with body weight support results in more independent walking than assisted overground walking in non-ambulatory patients early after stroke: A systematic review.
Journal of Physiotherapy, 56 (2010), pp. 153-161
[Arnold y Bautmans, 2014]
P. Arnold, I. Bautmans.
The influence of strength training on muscle activation in elderly persons: A systematic review and meta-analysis.
Experimental Gerontology, 58 (2014), pp. 58-68
[Benevides et al., 2008]
Benevides, A. B., Bastos Filho, T. F., Sarcinelli Filho, M., 2008. Mental Task Recognition Based on EEG for Commanding a Robotic Wheelchair. In: 3rd Applied Robotics and Collaborative Systems Engineering (Robocontrol 08). p. 8.
[Bertrand et al., 1985]
O. Bertrand, F. Perrin, J. Pernier.
A theoretical justification of the average reference in topographic evoked potential studies.
Electroencephalography and clinical neurophysiology, 62 (1985), pp. 462-464
[Botelho et al., 2015]
Botelho, T., Soprani, D., Schneider, P., Carvalho, C., Vargas, L., Frizera, A., 2015. Uma Proposta De Protocolo De Colocação De Sensores Inerciais Utilizando Alinhamento Virtual Para Aplicações Em Análise De Movimento De Membros Inferiores. In: Anais do V Encontro Nacional de Engenharia Biomecânica - ENEBI 2015. Uberlândia, Brasil, pp. 511-515.
[Cheng et al., 2004]
M. Cheng, W. Jia, X. Gao, S. Gao, F. Yang.
Mu rhythm-based cursor control: An offline analysis.
Clinical Neurophysiology, 115 (2004), pp. 745-751
[Gallego et al., 2012]
J.Á. Gallego, J. Ibáñez, J.L. Dideriksen, J.I. Serrano, M.D. del Castillo, D. Farina, E. Rocon.
A multimodal human-robot interface to drive a neuroprosthesis for tremor management.
IEEE Transactions on Systems, Man, and Cybernetics, 42 (2012), pp. 1159-1168
[Gourab y Schmit, 2010]
K. Gourab, B.D. Schmit.
Changes in movement-related u-band EEG signals in human spinal cord injury.
Clinical Neurophysiology, 121 (2010), pp. 2017-2023
[Guger et al., 2014]
C. Guger, T. Vaughan, B. Allison.
Brain-Computer Interface Research: A State-of-the-Art Summary 3. SpringerBriefs in Electrical and Computer Engineering.
Springer International Publishing, (2014), http://dx.doi.org/10.1007/978-3-319-09979-8
[Guyton y Hall, 2006]
A.C. Guyton, J.E. Hall.
Textbook of medical physiology.
11th Edition, Elsevier Saunders, (2006),
[Hudgins et al., 1993]
B. Hudgins, P. Parker, R.N. Scott.
A New Strategy for Multifunction Myoelectric Control.
IEEE Transactions on Biomedical Engineering, 40 (1993), pp. 82-94
[Husemann et al., 2007]
B. Husemann, F. Müller, C. Krewer, S. Heller, E. Koenig.
Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke: A randomized controlled pilot study.
[Ibáñez et al., 2013]
J. Ibáñez, J. Serrano, M. del Castillo, J. Gallego, E. Rocon.
Online detector of movement intention based on EEG-Application in tremor patients.
Biomedical Signal Processing and Control, 8 (nov 2013), pp. 822-829
[Jiang et al., 2015]
N. Jiang, L. Gizzi, N. Mrachacz-Kersting, K. Dremstrup, D. Farina.
A brain-computer interface for single-trial detection of gait initiation from movement related cortical potentials.
Clinical Neurophysiology, 126 (2015), pp. 154-159
[Ju et al., 2005]
M.-S. Ju, C.-C.K. Lin, D.-H. Lin, I.-S. Hwang, S.-M. Chen.
A Rehabilitation Robot With Force-Position Hybrid Fuzzy Controller: Hybrid Fuzzy Control of Rehabilitation Robot.
IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 13 (sep 2005), pp. 349-358
[Kirchner et al., 2014]
E.A. Kirchner, M. Tabie, A. Seeland.
Multimodal movement prediction -towards an individual assistance of patients.
PloS one, 9 (jan 2014), pp. e85060
[Latikka et al., 2001]
J.A. Latikka, J.A. Hyttinen, T.A. Kuurne, H.J. Eskola, J.A. Malmivuo.
Annual Reports of the Research Reactor Institute, Kyoto University, (2001), pp. 910-912 http://dx.doi.org/10.1109/IEMBS.2001.1019092
[Lu et al., 2010]
M.-K. Lu, P. Jung, B. Bliem, H.-T. Shih, Y.-T. Hseu, Y.-W. Yang, U. Ziemann, C.-H. Tsai.
The Bereitschaftspotential in essential tremor.
Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology, 121 (apr 2010), pp. 622-630
[Mackay y Mensah, 2004]
Mackay, J., Mensah, G., 2004. Global Burden of Stroke. In: The Atlas of Heart Disease and Stroke. World Health Organization, Ch. The Burden, pp. 50-51.
[Mrachacz-Kersting et al., 2012]
N. Mrachacz-Kersting, S.R. Kristensen, I. Niazi, D. Farina.
Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity.
The Journal of physiology, 590 (apr 2012), pp. 1669-1682
[Mrachacz-Kersting et al., 2017]
N. Mrachacz-Kersting, A.J.T. Stevenson, S. Aliakbaryhosseinabadi, A.C. Lundgaard, H.R. Jørgensen, K. Severinsen, D. Farina.
An Associative Brain-Computer-Interface for Acute Stroke Patients.
Springer International Publishing, (2017), pp. 841-845 http://dx.doi.org/10.1007/978-3-319-46669-9_137
[Müller-Putz et al., 2014]
G.R. Müller-Putz, I. Daly, V. Kaiser.
Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain-computer interface accuracy.
Journal of neural engineering, 11 (2014), pp. 035011
[Niazi et al., 2011]
I.K. Niazi, N. Jiang, O. Tiberghien, J.F. Nielsen, K. Dremstrup, D. Farina.
Detection of movement intention from single-trial movement-related cortical potentials.
Journal of Neural Engineering, 8 (oct 2011), pp. 066009
[Pfurtscheller y Da Silva, 1999]
G. Pfurtscheller, F.L. Da Silva.
Event-related EEG/MEG synchronization and desynchronization: basic principles.
Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology, 110 (nov 1999), pp. 1842-1857
[Shibasaki y Hallett, 2006]
H. Shibasaki, M. Hallett.
What is the Bereitschaftspotential?.
Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology, 117 (nov 2006), pp. 2341-2356
[Strong et al., 2007]
K. Strong, C. Mathers, R. Bonita.
Preventing stroke: saving lives around the world.
Lancet Neurology, 6 (2007), pp. 182-187
[Tsukahara et al., 2009]
Tsukahara, A., Hasegawa, Y., Sankai, Y., jun 2009. Standing-up motion support for paraplegic patient with Robot Suit HAL. 2009 IEEE International Conference on Rehabilitation Robotics, 211-217. DOI: 10.1109/ICORR.2009.5209567.
[Volkers et al., 2012]
K.M. Volkers, J.F. de Kieviet, H.P. Wittingen, E.J.A. Scherder.
Lower limb muscle strength (LLMS): Why sedentary life should never start?.
A review. Archives of Gerontology and Geriatrics, 54 (2012), pp. 399-414
[Winstein et al., 2016]
Winstein, C. J., Stein, J., Arena, R., Bates, B., Cherney, L. R., Cramer, S. C., Deruyter, F., Eng, J. J., Fisher, B., Harvey, R. L., Lang, C. E., Mackay-lyons, M., Ottenbacher, K. J., Pugh, S., Reeves, M. J., Richards, L. G., Otr, L., Stiers, W., Rp, A., 2016. AHA /ASA Guideline: Guidelines for Adult Stroke Rehabilitation and Recovery. DOI: 10.1161/STR.0000000000000098.
[Xu et al., 2014]
R. Xu, N. Jiang, C. Lin, N. Mrachacz-Kersting, K. Dremstrup, D. Farina.
Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications.
IEEE Transactions on Biomedical Engineering, 61 (2014), pp. 288-296
[Xu et al., 2016]
R. Xu, N. Jiang, N. Mrachacz-Kersting, K. Dremstrup, D. Farina.
Factors of influence on the performance of a short-latency non-invasive brain switch: Evidence in healthy individuals and implication for motor function rehabilitation.
Frontiers in Neuroscience, 9 (2016), pp. 1-9
[Yang y Kong, 2009]
Yang, S., Kong, L., 2009. Research on characteristic extraction of human gait. 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, 2-5. DOI: 10.1109/ICBBE.2009.5163328.
Opciones de artículo
Herramientas
es en pt
Política de cookies Cookies policy Política de cookies
Utilizamos cookies propias y de terceros para mejorar nuestros servicios y mostrarle publicidad relacionada con sus preferencias mediante el análisis de sus hábitos de navegación. Si continua navegando, consideramos que acepta su uso. Puede cambiar la configuración u obtener más información aquí. To improve our services and products, we use "cookies" (own or third parties authorized) to show advertising related to client preferences through the analyses of navigation customer behavior. Continuing navigation will be considered as acceptance of this use. You can change the settings or obtain more information by clicking here. Utilizamos cookies próprios e de terceiros para melhorar nossos serviços e mostrar publicidade relacionada às suas preferências, analisando seus hábitos de navegação. Se continuar a navegar, consideramos que aceita o seu uso. Você pode alterar a configuração ou obter mais informações aqui.