Torres-García AA, Reyes-García CA, Villaseñor-Pineda L, Ramírez-Cortés JM

Idioma: Español
Referencias bibliográficas: 38
Paginas: 23-39
Archivo PDF: 713.79 Kb.

RESUMEN

El presente trabajo tiene como objetivo interpretar las señales de EEG registradas durante la pronunciación imaginada de palabras de un vocabulario reducido, sin emitir sonidos ni articular movimientos (habla imaginada o no pronunciada) con la intención de controlar un dispositivo. Específicamente, el vocabulario permitiría controlar el cursor de la computadora, y consta de las palabras del lenguaje español: “arriba”, “abajo”, “izquierda”, “derecha”, y “seleccionar”. Para ello, se registraron las señales de EEG de 27 individuos utilizando un protocolo básico para saber a priori en qué segmentos de la señal la persona imagina la pronunciación de la palabra indicada. Posteriormente, se utiliza la transformada wavelet discreta (DWT) para extraer características de los segmentos que son usados para calcular la energía relativa wavelet (RWE) en cada una de los niveles en los que la señal es descompuesta, y se selecciona un subconjunto de valores RWE provenientes de los rangos de frecuencia menores a 32 Hz. Enseguida, éstas se concatenan en dos configuraciones distintas: 14 canales (completa) y 4 canales (los más cercanos a las áreas de Broca y Wernicke). Para ambas configuraciones se entrenan tres clasificadores: Naive Bayes (NB), Random Forest (RF) y Máquina de vectores de soporte (SVM). Los mejores porcentajes de exactitud se obtuvieron con RF cuyos promedios fueron 60.11% y 47.93% usando las configuraciones de 14 canales y 4 canales, respectivamente. A pesar de que los resultados aún son preliminares, éstos están arriba del 20 %, es decir, arriba del azar para cinco clases. Con lo que se puede conjeturar que las señales de EEG podrían contener información que hace posible la clasificación de las pronunciaciones imaginadas de las palabras del vocabulario reducido.

REFERENCIAS (EN ESTE ARTÍCULO)

1. Brumberg J. S., Nieto-Castanon A., Kennedy P. R. and Guenther F. H., “Brain–computer interfaces for speech communication,” Speech Communication, no. 52, p. 367–379, 2010.

2. Wolpaw J., Birbaumer N., McFarland D., Pfurtscheller G. and Vaughan T., “Braincomputer interfaces for communication and control,” Clinical neurophysiology, vol. 113, no. 6, pp. 767–791, 2002.

3. Sánchez de la Rosa J. L., Métodos para el procesamiento y análisis estadístico multivariante de señales multicanal: aplicación al estudio del EEG. PhD thesis, Universidad de La Laguna, España, 1993.

4. Bashashati A., Fatourechi M.,Ward R. and Birch G., “A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals,” Journal of Neural engineering, vol. 4, pp. R32–R57, 2007.

5. Pfurtscheller G., “Brain-computer interfaces: State of the art and future prospects,” in Proceedings of the 12th European Signal Processing Conference: EUROSIPCO 04, pp. 509–510, 2004.

6. Denby B., Schultz T., Honda K., Hueber T., Gilbert J. and Brumberg J., “Silent speech interfaces,” Speech Communication, vol. 52, no. 4, pp. 270–287, 2010.

7. Calliess J., “Further investigations on unspoken speech,” Master’s thesis, Institut für Theoretische Informatik Universität Karlsruhe (TH), Karlsruhe, Germany, 2006.

8. Porbadnigk A. and Schultz T., “EEGbased Speech Recognition: Impact of Experimental Design on Performance,” Master’s thesis, Institut für Theoretische Informatik Universität Karlsruhe (TH), Karlsruhe, Germany, 2008.

9. Suppes P., Lu Z. and Han B., “Brain wave recognition of words,” Proceedings of the National Academy of Sciences of the United States of America, vol. 94, no. 26, p. 14965, 1997.

10. Wand M., “Wavelet-based Preprocessing of Electroencephalographic and Electromyographic Signals for Speech Recognition,” Studienarbeit Lehrstuhl Prof. Waibel Interactive Systems Laboratories Carnegie Mellon University, Pittsburgh, PA, USA and Institut für Theoretische Informatik Universität Karlsruhe (TH), Karlsruhe, Germany, 2007.

11. Wester M. and Schultz T., “Unspoken Speech - Speech Recognition Based On Electroencephalography” Master’s thesis, Institut für Theoretische Informatik Universität Karlsruhe (TH), Karlsruhe, Germany, 2006.

12. Brigham K. and Kumar B., “Imagined Speech Classification with EEG Signals for Silent Communication: A Preliminary Investigation into Synthetic Telepathy” in Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on, pp. 1–4, IEEE, 2010.

13. DaSalla C. S., Kambara H., Koike Y. and Sato M., “Spatial filtering and single-trial classification of EEG during vowel speech imagery” in i-CREATe ’09: Proceedings of the 3rd International Convention on Rehabilitation Engineering & Assistive Technology, (New York, NY, USA), pp. 1– 4, ACM, 2009.

14. D’Zmura M., Deng S., Lappas T., Thorpe S. and Srinivasan R., “Toward EEG sensing of imagined speech” Human-Computer Interaction. New Trends, pp. 40–48, 2009.

15. Torres-García A. A., Reyes-García C. A. and Villaseñor-Pineda L., “Hacia la clasificación de habla no pronunciada mediante electroencefalogramas (EEG)” in XXXIV Congreso Nacional de Ingeniería Biomédica, pp. 9–12, 2011.

16. Torres-García A. A., Reyes-García C. A. and Villaseñor-Pineda L., “Toward a silent speech interface based on unspoken speech” in BIOSTEC - BIOSIGNALS (S. V. Huffel, C. M. B. A. Correia, A. L. N. Fred, and H. Gamboa, eds.), pp. 370–373, SciTePress, 2012.

17. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., and Witten I., “The weka data mining software: an update” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10–18, 2009.

18. Geschwind N., “Language and the brain.,” Scientific American, 1972.

19. Lotte F., Congedo M., Lécuyer A., Lamarche F. and Arnald B., “A review of classication algorithms for EEG-based brain-computer interfaces” Journal of Neural Engineering, vol. 4, pp. r1–r13, 2007.

20. Priestley M., “Wavelets and timedependent spectral analysis,” Journal of Time Series Analysis, vol. 17, no. 1, pp. 85– 103, 2008.

21. Pinsky M., Introduction to Fourier analysis and wavelets, vol. 102. Amer Mathematical Society, 2002.

22. Zhu Z., Jia S. and Ji Z., “Towards a Memetic Feature Selection Paradigm [Application Notes],” Computational Intelligence Magazine, IEEE, vol. 5, no. 2, pp. 41–53, 2010.

23. Michie D., Spiegelhalter D. and Taylor C., Machine Learning, Neural and Statistical Classification. 1994.

24. Jensen R. and Shen Q., Computational intelligence and feature selection: rough and fuzzy approaches. IEEE Press Series On Computational Intelligence, 2008.

25. Burges C., “A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery, vol. 2, no. 2, pp. 121–167, 1998.

26. Bennett K. and Campbell C., “Support vector machines: hype or hallelujah?,” ACM SIGKDD Explorations Newsletter, vol. 2, no. 2, pp. 1–13, 2000.

27. Schlögl A., Lee F., Bischof H. and Pfurtscheller G., “Characterization of fourclass motor imagery EEG data for the BCI-competition 2005,” Journal of Neural Engineering, vol. 2, p. L14, 2005.

28. Jain A., Duin R. and Mao J., “Statistical pattern recognition: A review,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 1, pp. 4–37, 2000.

29. Breiman L., “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.

30. Rokach L., Pattern Classification Using Ensemble Methods. World Scientific, 2009.

31. Dietterich T., “Ensemble methods in machine learning,” Multiple classifier systems, pp. 1–15, 2000.

32. Lal T. N., Schroder M., Hinterberger T., Weston J., Bogdan M., Birbaumer N., and Scholkopf B., “Support vector channel selection in BCI,” Biomedical Engineering, IEEE Transactions on, vol. 51, no. 6, pp. 1003–1010, 2004.

33. Guger C., Edlinger G., Harkam W., Niedermayer I., and Pfurtscheller G., “How many people are able to operate an EEGbased brain-computer interface (BCI)?,” Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 11, no. 2, pp. 145–147, 2003.

34. Allison B., Luth T., Valbuena D., Teymourian A., Volosyak I., and Graser A., “BCI Demographics: How many (and what kinds of) people can use an SSVEP BCI?,” Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 18, no. 2, pp. 107–116, 2010.

35. Volosyak I., Valbuena D., üth T. L, Malechka T., and Gräser A., “BCI Demographics II: How many (and what kinds of) people can use an SSVEP BCI,” IEEE Trans. Neural Syst. Rehabil. Eng, 2011.

36. Binder J., Frost J., Hammeke T., Cox R., Rao S., and Prieto T., “Human brain language areas identified by functional magnetic resonance imaging,” The Journal of Neuroscience, vol. 17, no. 1, pp. 353–362, 1997.

37. Hesslow G., “Conscious thought as simulation of behaviour and perception,” Trends in cognitive sciences, vol. 6, no. 6, pp. 242–247, 2002.

38. Hickok G. and Poeppel D., “The cortical organization of speech processing,” Nature Reviews Neuroscience, vol. 8, no. 5, pp. 393–402, 2007.