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Metodología para la Creación de una Interfaz Cerebro-Computador Aplicada a la Identificación de la Intención de Movimiento

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Metodología para la Creación de una Interfaz Cerebro-Computador Aplicada a la Identificación de la Intención de Movimiento

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Del Castillo, MD.; Serrano, JI.; Ibáñez, J.; Barrios, LJ. (2011). Metodología para la Creación de una Interfaz Cerebro-Computador Aplicada a la Identificación de la Intención de Movimiento. Revista Iberoamericana de Automática e Informática industrial. 8(2):93-102. https://doi.org/10.1016/S1697-7912(11)70030-9

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Metadatos del ítem

Título: Metodología para la Creación de una Interfaz Cerebro-Computador Aplicada a la Identificación de la Intención de Movimiento
Otro titulo: Methodology for building Brain-Computer Interfaces applied to identify voluntary movement intention
Autor: del Castillo, Mª D. Serrano, J. I. Ibáñez, J. Barrios, L. J.
Fecha difusión:
Resumen:
[EN] BCIs provide a channel for sending commands to the external environment by using electrophysiological measurements recording brain activity. The algorithm transforming the input signal into the output commands and the ...[+]


[ES] Las Interfaces Cerebro-Computador proporcionan un canal para enviar órdenes al mundo exterior haciendo uso de medidas electrofisiológicas de la actividad cerebral. En este artículo se presenta la combinación de un ...[+]
Palabras clave: Asynchronous BCI , User modelling , Adaption , Data mining , ERD , Interfaz Cerebro-Computador (ICC) asíncrona , Señal electroencefalográfica , Personalización , Ritmos sensorimotores , Minería de datos , Adaptación , Clasificación
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.1016/S1697-7912(11)70030-9
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.1016/S1697-7912(11)70030-9
Código del Proyecto:
info:eu-repo/grantAgreement/EC/FP7/224051/EU/An ambulatory BCI-driven tremor suppression system based on functional electrical stimulation/
info:eu-repo/grantAgreement/MEC//TEC2006-13966-C03-03/ES/INTEGRACION DE RESONANCIA MAGNETICA Y ELECTROENCEFALOGRAFIA. APLICACION AL FUNDAMENTO Y USO DE INTERFACES CEREBRO COMPUTADOR POR DISCAPACITADOS/
Agradecimientos:
Este trabajo ha sido realizado con la financiación de la Comisión de la Unión Europea dentro del 7 Programa Marco, para el proyecto con referencia ICT-2007-224051: “TREMOR: An ambulatory BCI-driven tremor suppression system ...[+]
Tipo: Artículo

References

Bai, O., Lin, P., Vorbach, S., Li, J., Furlani, S., & Hallett, M. (2007). Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG. Clinical Neurophysiology, 118(12), 2637-2655. doi:10.1016/j.clinph.2007.08.025

Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007). A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Journal of Neural Engineering, 4(2), R32-R57. doi:10.1088/1741-2560/4/2/r03

Daly, J. J., & Wolpaw, J. R. (2008). Brain–computer interfaces in neurological rehabilitation. The Lancet Neurology, 7(11), 1032-1043. doi:10.1016/s1474-4422(08)70223-0 [+]
Bai, O., Lin, P., Vorbach, S., Li, J., Furlani, S., & Hallett, M. (2007). Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG. Clinical Neurophysiology, 118(12), 2637-2655. doi:10.1016/j.clinph.2007.08.025

Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007). A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Journal of Neural Engineering, 4(2), R32-R57. doi:10.1088/1741-2560/4/2/r03

Daly, J. J., & Wolpaw, J. R. (2008). Brain–computer interfaces in neurological rehabilitation. The Lancet Neurology, 7(11), 1032-1043. doi:10.1016/s1474-4422(08)70223-0

Donchin, E., Spencer, K. M., & Wijesinghe, R. (2000). The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering, 8(2), 174-179. doi:10.1109/86.847808

Donoghue, J. P. (2002). Connecting cortex to machines: recent advances in brain interfaces. Nature Neuroscience, 5(S11), 1085-1088. doi:10.1038/nn947

Farina, D., Nascimento, O. F. do, Lucas, M.-F., & Doncarli, C. (2007). Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters. Journal of Neuroscience Methods, 162(1-2), 357-363. doi:10.1016/j.jneumeth.2007.01.011

Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510-523. doi:10.1016/0013-4694(88)90149-6

Fazli, S., Popescu, F., Danóczy, M., Blankertz, B., Müller, K.-R., & Grozea, C. (2009). Subject-independent mental state classification in single trials. Neural Networks, 22(9), 1305-1312. doi:10.1016/j.neunet.2009.06.003

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34. doi:10.1145/240455.240464

Flexer, A., & Bauer, H. (2000). Monitoring human information processing via intelligent data analysis of EEG recordings. Intelligent Data Analysis, 4(2), 113-128. doi:10.3233/ida-2000-4203

Graimann, B., Huggins, J. ., Levine, S. ., & Pfurtscheller, G. (2002). Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clinical Neurophysiology, 113(1), 43-47. doi:10.1016/s1388-2457(01)00697-6

Gu, Y., Dremstrup, K., & Farina, D. (2009). Single-trial discrimination of type and speed of wrist movements from EEG recordings. Clinical Neurophysiology, 120(8), 1596-1600. doi:10.1016/j.clinph.2009.05.006

Isa, T., Fetz, E. E., & Müller, K.-R. (2009). Recent advances in brain–machine interfaces. Neural Networks, 22(9), 1201-1202. doi:10.1016/j.neunet.2009.10.003

Kübler, A., Kotchoubey, B., Kaiser, J., Wolpaw, J. R., & Birbaumer, N. (2001). Brain–computer communication: Unlocking the locked in. Psychological Bulletin, 127(3), 358-375. doi:10.1037/0033-2909.127.3.358

Kutas, M., & Hillyard, S. A. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307(5947), 161-163. doi:10.1038/307161a0

Mason, S. G., & Birch, G. E. (2000). A brain-controlled switch for asynchronous control applications. IEEE Transactions on Biomedical Engineering, 47(10), 1297-1307. doi:10.1109/10.871402

Müller-Putz, G. R., Scherer, R., Pfurtscheller, G., & Rupp, R. (2005). EEG-based neuroprosthesis control: A step towards clinical practice. Neuroscience Letters, 382(1-2), 169-174. doi:10.1016/j.neulet.2005.03.021

Pfurtscheller, G. (1977). Graphical display and statistical evaluation of event-related desynchronization (ERD). Electroencephalography and Clinical Neurophysiology, 43(5), 757-760. doi:10.1016/0013-4694(77)90092-x

Pfurtscheller, G., & Aranibar, A. (1979). Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalography and Clinical Neurophysiology, 46(2), 138-146. doi:10.1016/0013-4694(79)90063-4

Pfurtscheller, G. (1992). Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. Electroencephalography and Clinical Neurophysiology, 83(1), 62-69. doi:10.1016/0013-4694(92)90133-3

Pfurtscheller, G., Sager, W., & Wege, W. (1981). Correlations between CT scan and sensorimotor EEG rhythms in patients with cerebrovascular disorders. Electroencephalography and Clinical Neurophysiology, 52(5), 473-485. doi:10.1016/0013-4694(81)90032-8

Pfurtscheller, G., Neuper, C., Guger, C., Harkam, W., Ramoser, H., Schlogl, A., … Pregenzer, M. (2000). Current trends in Graz brain-computer interface (BCI) research. IEEE Transactions on Rehabilitation Engineering, 8(2), 216-219. doi:10.1109/86.847821

Pfurtscheller, G., & Neuper, C. (1994). Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neuroscience Letters, 174(1), 93-96. doi:10.1016/0304-3940(94)90127-9

Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110(11), 1842-1857. doi:10.1016/s1388-2457(99)00141-8

Pfurtscheller, G., Brunner, C., Schlögl, A., & Lopes da Silva, F. H. (2006). Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage, 31(1), 153-159. doi:10.1016/j.neuroimage.2005.12.003

Sannelli, C., Braun, M., & Müller, K.-R. (2009). Improving BCI performance by task-related trial pruning. Neural Networks, 22(9), 1295-1304. doi:10.1016/j.neunet.2009.08.006

Serrano J.I., del Castillo M.D. (2005). BCI Competition III. http://www.bbci.de/competition/iii/results/index.html

Townsend, G., Graimann, B., & Pfurtscheller, G. (2004). Continuous EEG Classification During Motor Imagery—Simulation of an Asynchronous BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(2), 258-265. doi:10.1109/tnsre.2004.827220

Townsend, G., Graimann, B., & Pfurtscheller, G. (2006). A Comparison of Common Spatial Patterns With Complex Band Power Features in a Four-Class BCI Experiment. IEEE Transactions on Biomedical Engineering, 53(4), 642-651. doi:10.1109/tbme.2006.870237

Wang, Y., Zhang, Z., Li, Y., Gao, X., Gao, S., & Yang, F. (2004). BCI Competition 2003—Data Set IV: An Algorithm Based on CSSD and FDA for Classifying Single-Trial EEG. IEEE Transactions on Biomedical Engineering, 51(6), 1081-1086. doi:10.1109/tbme.2004.826697

Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., … Vaughan, T. M. (2000). Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 8(2), 164-173. doi:10.1109/tre.2000.847807

Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791. doi:10.1016/s1388-2457(02)00057-3

Wolpaw, J. R., & McFarland, D. J. (2004). Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proceedings of the National Academy of Sciences, 101(51), 17849-17854. doi:10.1073/pnas.0403504101

Yang, J., & Honavar, V. (1998). Feature subset selection using a genetic algorithm. IEEE Intelligent Systems, 13(2), 44-49. doi:10.1109/5254.671091

Yom-Tov, E., & Inbar, G. F. (2003). Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface. Medical & Biological Engineering & Computing, 41(1), 85-93. doi:10.1007/bf02343543

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