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

Mª D. del Castillo; J. I. Serrano; J. Ibáñez; L. J. Barrios

doi:10.1016/S1697-7912(11)70030-9

Autores/as

Mª D. del Castillo CSIC
J. I. Serrano CSIC
J. Ibáñez CSIC
L. J. Barrios CSIC

DOI:

https://doi.org/10.1016/S1697-7912(11)70030-9

Palabras clave:

Interfaz Cerebro-Computador (ICC) asíncrona, señal electroencefalográfica, personalización, ritmos sensorimotores, minería de datos, adaptación, clasificación

Resumen

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 método de selección de características y un algoritmo de clasificación probabilístico para construir el modelo predictivo de la intención anticipada de movimiento voluntario de pacientes con temblor a partir de un solo ensayo. Los resultados obtenidos muestran una potencial de discriminación del 70%, una tasa de error aceptable (6.6%) y una rápida respuesta (cada 250 ms), lo que indica que esta combinación es una buena base para la construcción de ICCs que no requieran entrenamiento del usuario de forma personalizada, asíncrona y adaptativa.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Mª D. del Castillo, CSIC

Grupo de Bioingeniería

J. I. Serrano, CSIC

Grupo de Bioingeniería

J. Ibáñez, CSIC

Grupo de Bioingeniería

L. J. Barrios, CSIC

Grupo de Bioingeniería

Citas

Bai O., Lin P., Vorbach S., Li J., Furlani S. and Hallet M. (2007). Exploration of computational methods for classification of movement intention during human voluntary movement form single trial EEG. Clinical Neurophysiology, 118, 2637-2655.

Bashashati A., Fatourechi M. and Ward R.K. (2007). A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Engineering, 4, 32-57.

Daly J.J. and Wolpaw J.R. (2008). Brain–computer interfaces in neurological rehabilitation. The Lancet Neurology, 7(11), 1032–1043.

Domingos, P. and Pazzani, M. (1996). Beyond independence: conditions for the optimality of the simple Bayesian classifier. Proc. 13th International Conference on Machine Learning, 105-112.

Donchin E., Spencer K.M. and Wijensighe R. (2000). The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering, 8, 174-179.

Donoghue J.P. (2002). Connecting cortex to machines: recent advances in brain interfaces. Natural Neuroscience, 5, 1085-1088.

Drongelen, W.v. (2007). Signal processing for neuroscientists, an introduction to the analysis of physiological signals. Elsevier.

Farina D., do Nascimento O.F., Lucas M.F. and Doncarli C. (2007). Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters. J. Neuroscience Methods, 162, 367-363.

Farwell L.A. and Donchin E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70, 510-523.

Fazli S., Popescu F., Danóczy M., Blankertz B., Müller K.R. and Grozea C. (2009). Subject-independent mental state classification in single trials. Neural Networks, 22, 1305- 1312.

Fayyad U.M., Piatetsky-Shapiro G. and Smyth P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.

Flexer A. and Bauer H. (2000). Monitoring human information processing via intelligent data analysis of EEG recordings. Intelligent Data Analysis, 4(2), 113-128.

Goldberg D. (1989). Genetic algorithms in search, optimization and machine learning. Addison Wesley.

Grainmann B., Huggins J.E., Levine S.P. and Pfurtscheller G. (2002). Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clinical Neurophysiology, 113, 43-47.

Gu Y., Dremstrup K. and Farina D. (2009). Single-trial discrimination of type and speed of wrist movements from EEG recordings. Clinical Neurophysiology, 120, 1596- 1600.

Isa T., Fetz E.E. and Müller K.R. (2009). Recent advances in brain-machine interfaces. Neural Networks, 22, 1201-1202.

Kubler A., Kotchoubey B., Kaiser J., Wolpaw J.R. and Birbaumer N. (2000). Brain computer communication: unlocked the locked. Psychology Bulletin, 127(3), 358-375.

Kutas M., and Hillyard, S.A. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307, 161-163.

Mason S.G. and Birch G.E. (2000). A brain controlled switch for asynchronous control applications. IEEE Transactions on Biomedical Engineering, 47, 1297-1307.

Millán J., Mouriño R., Cincotti F., Babiloni F., Varsta M. and Heikkonen J. (2000). Local neural classifier for EEG-based recognition of mental tasks. Proc. IEEE International Joint Conference on Neural Networks, 632-636.

Mitchell, T.M. (1997). Machine learning. McGraw-Hill.

Müller G.R., Scherer R., Pfurtscheller G. and Rupp R. (2005). EEG-based neuroprosthesis control: a step towards clinical practice. Neuroscience Letters, 382, 169–174.

Pfurtscheller G. (1977). Graphical display and statistical evaluation of event-related desynchronization (ERD). Electroencephalography and Clinical Neurophysioogy. 43, 757–760.

Pfurtscheller G. and Aranibar A. (1979). Evaluation of eventrelated desynchronization (ERD) preceding and following voluntary self-paced movements. Electroencephalography and Clinical Neurophysiology, 46, 138-146.

Pfurtscheller G. (1992). Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. Electroencephalography and Clinical Neurophysiology, 83, 62-69.

Pfurtscheller G., Sager G. and Wege W. (1981). Correlations between CT scan and sensorimotor EEG rhythms in patients with cerebral vascular disorders. Electroencephalography and Clinical Neurophysiology, 52, 473-485.

Pfurtscheller G., Neuper C., Guger C., Harkam W., Ramoser H., Schlogl A., Obermaier B. and Pregenzer M. (2000). Current trends in Graz brain-computer interface (BCI) research. IEEE Transactions on Rehabilitation Engineering, 8, 216- 219.

Pfurstscheller G. and Neuper C. (1994). Event-related desynchronization of mu rhythm in the EEG over the cortical hand area in man. Neuroscience Letters, 174, 93-96.

Pfurtscheller G. and Lopes da Silva F.H. (1999). Event-related EEG/EMG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110, 1842–1857.

Pfurtscheller G., Brunner C., Schlögl A. and da Silva F.H. (2006). Mu rhythm (de)synchronization and EEG singletrial classification of different motor imagery tasks. Neuroimage, 31, 153–159.

Rocón E., Gallego J. A., Barrios L., Victoria A. R., Ibánez J., Farina D., Negro F., Dideriksen J. L., Conforto S., D'Alessio T., Severini G., Belda-Lois J. M., Popovic L. Z., Grimaldi G., Manto M. and Pons J. L. (2010). Multimodal BCI-mediated FES suppression of pathological tremor. Proc. 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3337- 3340.

Sannelli C., Braun M. and Müller K.R. (2009). Improving BCI performance by task-related trial pruning. Neural Networks, 22, 1295-1304.

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

Townsend G., Grainmann B. and Pfurtscheller G. (2004). Continuous EEG classification during motor imagerysimulation of an asynchronous BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(2), 258–265.

Townsend G., Grainmann B. and 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, 642–651.

Wang Y., Zhang Z., Li Y., Gao X., Gao S and 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, 1081-1086.

Wolpaw J.R., Birbaumer M., Heetderks W.J., McFarland D.J., Peckham P.H., Schalk G., Donchin E., Quatrano L.A., Robinson J. and Vaughan T.M. (2000). Brain-computer interface technology: a review of the first international Meeting. IEEE Transactions on Rehabilitation Engineering, 8(2), 164-173.

Wolpaw J.R., Birbaumer M., McFarland D.J., Pfurtscheller G. and Vaughan T.M. (2002). Brain computer interfaces for communication and control. Clinical Neurophysiology, 113, 767-791.

Wolpaw J.R. and McFarland D.J. (2004). Control of a two dimensional movement signal by a noninvasive brain– computer interface in humans. Proc. National Academy of Sciences, 101(51), 17849-17854.

Yang J. and Honavar V. (1998). Feature subset selection using a genetic algorithm. IEEE Intelligent Systems and their Applications. 13(2), 44-49.

Yom-Tov E. and Inbar G.F. (2003). Detection of movementrelated potentials from the electro-encephalogram for possible use in a brain-computer interface. Medical & Biological Engineering & Computing, 41, 85-93.