Silent speech of vowels in persons of different cognitive styles

• Autores: Omar López Vargas, Luis Sarmiento Vela, Jan Bacca Rodríguez, Sergio Iván Villamizar Delgado, Jhon Fredy Sarmiento Vela
• Localización: Suma Psicológica, ISSN-e 0121-4381, Vol. 29, Nº. 1, 2022, págs. 20-29
• Idioma: inglés
• DOI: 10.14349/sumapsi.2022.v29.n1.3
• Títulos paralelos:
  • Habla silenciosa de las vocales en personas de diferente estilo cognitivo
• Enlaces
  • Texto completo
• Resumen
  • English

    Introduction: This research measures the differences in silent speech of the vowels / a / – / u / in Spanish, in students with different cognitive styles in the Field Dependence – Independence (FDI) dimension. Method: Fifty-one (51) adults participated in the study. Electroencephalographic (EEG) signals were taken from 14 electrodes placed on the scalp in the language region located in the left hemisphere. Previously, the embedded figures test (EFT) was applied in order to classify them into dependent, intermediate and field independent persons. To analyse the EEG data, the signals were decomposed into intrinsic mode functions (IMF) and a mixed repeated measures analysis was performed. Results: It was found that the Power Spectral Density (PSD) in the vowels is independent of the cognitive style and its magnitude depends on the position of the electrodes. Conclusions: The results suggest that there are no significant differences in PSDs in the silent speech of vowels /a/-/u/ in persons of different cognitive styles. Significant differences were found in the PSDs according to the position of the 14 electrodes used. In our configuration, the silent speech of vowels can be studied using electrodes placed in premotor, motor and Wernicke areas.

  • português

    ntroducción: La investigación mide las diferencias en el habla silenciosa de las vocales /a/-/u/ en español, en estudiantes de diferente estilo cognitivo en la dimensión Dependencia – Independencia de campo (DIC). Método: En el estudio participaron 51 adultos. Se tomaron señales electroencefalográficas (EEG), a partir de 14 electrodos dispuestos sobre el cuero cabelludo de la región del lenguaje ubicada en el hemisferio izquierdo. Previamente les fue aplicado el test de figuras enmascaradas EFT con el fin de clasificarlos en personas dependientes, intermedios e independientes de campo. Para analizar los datos del EEG se descompusieron las señales en funciones de modo intrínseco (IMF) y se realizó un análisis mixto de medidas repetidas. Resultados: Se halló que la densidad espectral de potencia (PSD) en las vocales es independiente del estilo cognitivo y su magnitud depende de la posición de los electrodos. Conclusión: Los resultados sugieren que no existen diferencias significativas en los PSD en el habla silenciosa de las vocales /a/-/u/ en las personas de diferente estilo cognitivo. Se hallaron diferencias significativas en los PSD de acuerdo con la posición de los 14 electrodos utilizados. En nuestra configuración, el habla silenciosa de las vocales puede ser estudiada mediante electrodos situados en las áreas premotora, motora y de Wernicke.

• Referencias bibliográficas
  • Callan, D. E., Callan, A. M., Honda, K., & Masaki, S. (2000). Single-sweep EEG analysis of neural processes underlying perception and...
  • Chi, X., Hagedorn, J. B., Schoonover, D., & Zmura, M. D. (2011). EEG-Based discrimination of imagined speech phonemes. International Journal...
  • Cooney, C., Folli, R., & Coyle, D. (2018). Neurolinguistics research advancing development of a direct-speech brain-computer interface....
  • Cooney, C., Korik, A., Folli, R., & Coyle, D. (2020). Evaluation of hyperparameter optimization in machine and deep learning methods for...
  • D’Zmura, M., Deng, S., Lappas, T., Thorpe, S., & Srinivasan, R. (2009). Toward EEG sensing of imagined speech. Lecture Notes in Computer...
  • DaSalla, C. S., Kambara, H., Sato, M., & Koike, Y. (2009). Single-trial classification of vowel speech imagery using common spatial patterns....
  • Evans, C., Richardson, J. T. E., & Waring, M. (2013). Field independence: Reviewing the evidence. British Journal of Educational Psychology,...
  • Fujimaki, N., Takeuchi, F., Kobayashi, T., Kuriki, S., & Hasuo, S. (1994). Event-related potentials in silent speech. Brain Topography,...
  • Geschwind, N. (1965). Disconnection syndromes in animals and man. Brain, 88(2), 237-294. [ Links ]
  • Ghosh, R., Sinha, N., Biswas, S. K., & Phadikar, S. (2019). A modified grey wolf optimization based feature selection method from EEG...
  • González-Castañeda, E. F., Torres-García, A. A., Reyes-García, C. A., & Villaseñor-Pineda, L. (2017). Sonification and textification:...
  • Graimann, B., Allison, B. Z., & Pfurtscheller, G. (2010). Brain-Computer interfaces: Revolutionizing human-computer interaction (1st ed.)....
  • Hansen, S. T., Hemakom, A., Gylling Safeldt, M., Krohne, L. K., Madsen, K. H., Siebner, H. R., Mandic, D. P., & Hansen, L. K. (2019)....
  • Hederich-Martínez, C., López-Vargas, O., & Camargo-Uribe, A. (2016). Effects of the use of a flexible metacognitive scaffolding on self-regulated...
  • Hemakom, A., Goverdovsky, V., Looney, D., & Mandic, D. P. (2016). Adaptive-projection intrinsically transformed multivariate empirical...
  • Ikeda, S., Shibata, T., Nakano, N., Okada, R., Tsuyuguchi, N., Ikeda, K., & Kato, A. (2014). Neural decoding of single vowels during covert...
  • Imanaka, M., Kakigi, R., & Nakata, H. (2017). The relationship between cognitive style and event-related potentials during auditory and...
  • Iqbal, S., Khan, Y. U., & Farooq, O. (2015). EEG based classification of imagined vowel sounds. 2015 International Conference on Computing...
  • Jahangiri, A., & Sepulveda, F. (2019). Correction to: The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production,...
  • Jia, S., Zhang, Q., & Li, S. (2014). Field dependence-independence modulates the efficiency of filtering out irrelevant information in...
  • Lee, W., Seong, J. J., Ozlu, B., Shim, B. S., Marakhimov, A., & Lee, S. (2021). Biosignal sensors and deep learning-based speech recognition:...
  • Li, R., Johansen, J. S., Ahmed, H., Ilyevsky, T. V., Wilbur, R. B., Bharadwaj, H. M., & Siskind, J. M. (2018). Training on the test set?...
  • Li, Y., & Wong, K. M. (2013). Riemannian distances for signal classification by power spectral density. IEEE Journal on Selected Topics...
  • López-Vargas, O., Ortiz-Vásquez, J., & Ibáñez-Ibáñez, J. (2020). Autoeficacia y logro de aprendizaje en estudiantes con diferente estilo...
  • López, O., Hederich, C., & Camargo, A. (2012). Logro de aprendizaje en ambientes hipermediales: Andamiaje autorregulador y estilo cognitivo....
  • Martin, S., Brunner, P., Holdgraf, C., Heinze, H. J., Crone, N. E., Rieger, J., Schalk, G., Knight, R. T., & Pasley, B. N. (2014). Decoding...
  • Matsumoto, M., & Hori, J. (2013). Classification of silent speech using adaptive collection. Proceedings of the 2013 IEEE Symposium on...
  • Matsumoto, M., & Hori, J. (2014). Classification of silent speech using support vector machine and relevance vector machine. Applied Soft...
  • Min, B., Kim, J., Park, H. J., & Lee, B. (2016). Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram....
  • Morooka, T., Ishizuka, K., & Kobayashi, N. (2018). Electroencephalographic Analysis of Auditory Imagination to Realize Silent Speech BCI....
  • Nguyen, C. H., Karavas, G. K., & Artemiadis, P. (2018). Inferring imagined speech using EEG signals: a new approach using Riemannian manifold...
  • Oltman, P. K., Semple, C., & Goldstein, L. (1979). Cognitive style and interhemispheric differentiation in the EEG. Neuropsychologia,...
  • Poeppel, D., & Hickok, G. (2004). Towards a new functional anatomy of language. Cognition, 92(1-2), 112. https://doi.org/10.1016/j.cognition.2003.11.001...
  • Pressel Coretto, G. A., Gareis, I. E., & Rufiner, H. L. (2017). Open access database of EEG signals recorded during imagined speech. 12th...
  • Proakis, J. G., & Manolakis, D. G. (2007). Digital signal processing: Principles, algorithms, and applications (4th ed.). Pearson Education....
  • Qureshi, M. N. I., Min, B., Park, H. J., Cho, D., Choi, W., & Lee, B. (2018). Multiclass classification of word imagination speech with...
  • Rashid, M., Sulaiman, N., P. P. Abdul Majeed, A., Musa, R. M., Ahmad, A. F., Bari, B. S., & Khatun, S. (2020). Current status, challenges,...
  • Riaz, A., Akhtar, S., Iftikhar, S., Khan, A. A., & Salman, A. (2015). Inter comparison of classification techniques for vowel speech imagery...
  • Sarmiento, L. C., Lorenzana, P., Cortés, C. J., Arcos, W. J., Bacca, J. A., & Tovar, A. (2014). Brain computer interface (BCI) with EEG...
  • Sarmiento, L. C., Villamizar, S., López, O., Collazos, A. C., Sarmiento, J., & Rodríguez, J. B. (2021). Recognition of eeg signals from...
  • Solórzano-Restrepo, J., & López-Vargas, O. (2019). Efecto diferencial de un andamiaje metacognitivo en un ambiente e-learning sobre la...
  • Valencia-Vallejo, N., López-Vargas, O., & Sanabria-Rodríguez, L. (2019). Effect of a metacognitive scaffolding on self-efficacy, metacognition,...
  • Villamizar, S. I., Sarmiento, L. C., López, O., Caballero, J., & Bacca, J. (2021). EEG vowel silent speech signal discrimination based...
  • Wang, L., Zhang, X., Zhong, X., & Zhang, Y. (2013). Analysis and classification of speech imagery EEG for BCI. Biomedical Signal Processing...
  • Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-Dependent and Field-Independent Cognitive Styles and Their...
  • Yoshimura, N., Nishimoto, A., Belkacem, A. N., Shin, D., Kambara, H., Hanakawa, T., & Koike, Y. (2016). Decoding of covert vowel articulation...
  • Yuan, R., Lv, Y., & Song, G. (2018). Multi-fault diagnosis of rolling bearings via adaptive projection intrinsically transformed multivariate...
  • Zoccolotti, P. (1982). Field dependence, laterality and the EEG: A reanalysis of O’Connor and Shaw Biological Psychology, 15, 203-207. [ Links...