Fuzzy Modular Neural Model for Blinking Coding Detection and Classification for Linguistic Expression Recognition

  • Mario I. Chacon-MurguiaEmail author
  • Carlos E. Cañedo-Figueroa
  • Juan A. Ramirez-Quintana
Part of the Studies in Computational Intelligence book series (SCI, volume 862)


EEG signal analysis provides a new alternative to implement brain computer interfaces. Among the possible signals that can be used for brain computer interfaces are signals generated during blinking. This chapter presents a novel fuzzy modular neural model for linguistic expression recognition using blinking coding detection and classification. The linguistic expressions are coded into blinking sequences, and the proposed system analyzes these sequences to detect possible existence of events, expression codes. The blinking signals are first preprocessed to eliminate possible offset and to limit their bandwidth. Then, a new processing step obtains statistical information of the signals and makes them invariant to future users. If a code expressions is detected, it is processed to generate a feature vector with statistical and frequency features. The feature vector is classified for a set of specialized modular neural networks, and finally an output analysis scheme is used to reduce an improper decision. The fuzzy detection systems was tested with signals corresponding to blinking codes, involuntary blinking, and noise and achieved 100% of correct detection, and the final classification of expression with the modular network was 94.26%. Regarding these results, the propose system is considered suitable for applications with seriously impaired persons to establish a basic communication with other person through blinking code generation.


Brain computer interface Artificial neural network Fuzzzy logic Fuzzy neural model Blinking recognition 



The authors greatly appreciate the support of Tecnologico Nacional de Mexico under grant 5684.16-P to develop this work.


  1. 1.
    Lance, B.J., Kerick, S.E., Ries, A.J., Oie, K.S., McDowell, K.: Brain-computer interface technologies in the coming decades. Proc. IEEE 100(3), 1585–1599 (2012)CrossRefGoogle Scholar
  2. 2.
    Saeedi, S., Chavarriaga, R., Leeb, R., Millán, J.R.: Adaptive assistance for brain-computer interfaces by online prediction of command reliability. IEEE Comput. Intell. Mag. 11(1), 32–39 (2016)CrossRefGoogle Scholar
  3. 3.
    All in the Mind Engineering & Technology Magazine. Available at Accessed 15 Jan 2017
  4. 4.
    Abbass, H., Guan, C., Chen-Tan, K.: Computational intelligence for brain computer interface. IEEE Comput. Intell. Mag. 11(1), 18 (2016)CrossRefGoogle Scholar
  5. 5.
    Cavrini, F., Bianchi, L., Quitadamo, L.R., Saggio, G.: A fuzzy integral ensemble method in visual P300 brain-computer interface. Comput. Intell. Neurosci. 2016(8), 1–9 (2016)CrossRefGoogle Scholar
  6. 6.
    Formaggio, E., Masiero, S., Bosco, A., Izzi, F., Piccione, F., Del Felice, A.: Quantitative EEG evaluation during robot-assisted foot movement. IEEE Trans. Neural Syst. Rehabil. Eng. 25(9), 1633–1640 (2017)CrossRefGoogle Scholar
  7. 7.
    Zhang, W., Sun, F., Tan, C., Liu, S.: Low-rank linear dynamical systems for motor imagery EEG. Comput. Intell. Neurosci. 2016(12), 1–7 (2016)Google Scholar
  8. 8.
    Abdulkader, N.S., Ayman, A., Mostafa-Sami, M.: Brain computer interfacing: applications and challenges. Egypt. Inform. J. 16(2), 213–230 (2015)CrossRefGoogle Scholar
  9. 9.
    Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M.: Transfer learning in brain-computer interfaces. IEEE Comput. Intell. Mag. 2015(12), 1–20 (2015)Google Scholar
  10. 10.
    Amali-Rani, B.J., Umamakeswari, A., Sree-Madhubala, J.: An approach toward wireless brain–computer interface system using EEG signals: a review. Natl. J. Physiol. Pharm. Pharmacol. 5(5), 350–356 (2015)CrossRefGoogle Scholar
  11. 11.
    Soman, S., Murthy, B.K.: Using brain computer interface for synthesized speech communication for the physically disabled. Proc. Comput. Sc. 46, 292–298 (2015)CrossRefGoogle Scholar
  12. 12.
    Roy, R.N., Charbonnierb, S., Bonnet, S.: Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms. Biomed. Signal Process. Control 14(11), 256–264 (2014)CrossRefGoogle Scholar
  13. 13.
    Zhanga, C., Wua, X., Zhanga, L., Hea, X., Lva, Z.: Simultaneous detection of blink and heart rate using multi-channel ICA from smart phone videos. Biomed. Signal Process. Control 33(3), 189–200 (2017)CrossRefGoogle Scholar
  14. 14.
    Singh, B., Wagatsuma, H.A.: Removal of eye movement and blink artifacts from EEG data using morphological component analysis. Comput. Math. Methods Med. 2017(2), 1–17 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kruis, A., Slagter, H.A., Bachhuber, D.R.W., Davidson, R.J.: Effects of meditation practice on spontaneous eye blink rate. Soc. Psychophysiological Res. 53(5), 749–758 (2016)Google Scholar
  16. 16.
    Kompoliti, K.: Cognitive assessments and Parkinson’s disease. The Encyclopedia of Movement Disorders, pp. 231–236. Elsevier, Academic Press, Chicago (2015)Google Scholar
  17. 17.
    Sepulveda, R., Montiel, M.O., Díaz, G., Gutierrez, D., Castillo, O.: EEG signal classification using artificial neural networks. Comput. Syst. 19(1), 69–88 (2015)Google Scholar
  18. 18.
    Manasi, S.D., Trivedi, A.R.: Gate/source-overlapped heterojunction tunnel FET-based LAMSTAR neural network and its application to EEG signal classification. In: Proceedings of International Joint Conference on Neural Networks (2016)Google Scholar
  19. 19.
    Chacon-Murguia, M.I., Quezada-Holguín, Y., Rivas-Perea, P., Cabrera, S.: Dust storm detection using a neural network with uncertainty and ambiguity output analysis. Lecture Notes in Computer Science. 21587(2_33), pp 305–313 (2011)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mario I. Chacon-Murguia
    • 1
    Email author
  • Carlos E. Cañedo-Figueroa
    • 1
  • Juan A. Ramirez-Quintana
    • 1
  1. 1.Visual Perception Applications on Robotic LabTecnologico Nacional de Mexico/I. T. ChihuahuaMexico CityMexico

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