Advertisement

Selection of Mental Tasks for Brain-Computer Interfaces Using NASA-TLX Index

  • Jhon Freddy Moofarry
  • Kevin Andrés Suaza Cano
  • Diego Fernando Saavedra Lozano
  • Javier Ferney Castillo GarcíaEmail author
Conference paper
  • 45 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

The brain-computer interfaces - BCIs allow people with disabilities to interact with the outside world using different communication channels than conventional ones. This article deals with the selection of tasks in the protocols for the development of BCI based on the paradigm of mental tasks. It is proposed to use the NASA-TLX index to evaluate the effect of the mental load of each of the tasks and contrast the performance of the interface task by task. In the implementation of BCI, the OPENBCI hardware was used for signal acquisition and the MATLAB software for processing. Five mental tasks were defined that activated different regions of the cerebral cortex. The acquisition protocol consisted of defining the rest time, execution and recovery for the tasks. The extraction methods used temporal, frequency and time-frequency combination characteristics. The classifiers used were neural networks, nearby neighbors and support vector machines. The evaluation of the TLX index seeks to quantify the appreciation of the effort, frustration and complexity of the task, therefore after the acquisition of signals for each task, the participant proceeded to evaluate the mental overload using the NASA-TLX index. The results obtained show that those tasks that require greater complexity to be performed presented a greater repeatability and higher success rate.

Keywords

Task mental BCI NASA-TLX index Workload 

References

  1. 1.
    Wolpaw, J., et al.: Brain-computer interface technology: a review of the first international meeting (2000)Google Scholar
  2. 2.
    Wolpaw, J., Birbaumer, N., Mcfarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)CrossRefGoogle Scholar
  3. 3.
    Kübler, A., et al.: Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64(10), 1775–1777 (2005)CrossRefGoogle Scholar
  4. 4.
    Wolpaw, J., McFarland, D.J.: Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl. Acad. Sci. U.S.A. 101(51), 17849–17854 (2004)CrossRefGoogle Scholar
  5. 5.
    Leuthardt, E., Schalk, G., Wolpaw, J.R., Ojemann, J.G., Moran, D.W.: A brain-computer interface using electrocorticographic signals in humans. J. Neural Eng. 1(2), 63–71 (2004)CrossRefGoogle Scholar
  6. 6.
    Felton, E., Wilson, J.A., Williams, J.C., Garell, P.C.: Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants: report of four cases. J. Neurosurg. 106(3), 495–500 (2007)CrossRefGoogle Scholar
  7. 7.
    Hochberg, L., et al.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099), 164–171 (2006)CrossRefGoogle Scholar
  8. 8.
    Kennedy, P., Bakay, R.A.E., Moore, M.M., Adams, K., Goldwaithe, J.: Direct control of a computer from the human central nervous system. IEEE Trans. Rehabil. Eng. 8(2), 198–202 (2000)CrossRefGoogle Scholar
  9. 9.
    Curran, E., Stokes, M.J.: Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems. Brain Cogn. 51(3), 326–336 (2003)CrossRefGoogle Scholar
  10. 10.
    Riccio, A., et al.: Workload measurement in a communication application operated through a P300-based brain-computer interface. J. Neural Eng. 8(2), 1–6 (2011)Google Scholar
  11. 11.
    Parasuraman, R., Hancock, P.A.: Adaptive control of mental workload. In: Stress, Workload, and Fatigue, pp. 305–320. Lawrence Erlbaum Associates Publishers, Mahwah (2001)Google Scholar
  12. 12.
    Wickens, C.D., Hollands, J.G., Banbury, S., Parasuraman, R.: Engineering Psychology and Human Performance, vol. 13, no. 1 (1987)Google Scholar
  13. 13.
    Hjortskov, N., Rissén, D., Blangsted, A.K., Fallentin, N., Lundberg, U., Søgaard, K.: The effect of mental stress on heart rate variability and blood pressure during computer work. Eur. J. Appl. Physiol. 92(1–2), 84–89 (2004)CrossRefGoogle Scholar
  14. 14.
    Hart, S., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv. Psychol 52, 139–183 (1988)CrossRefGoogle Scholar
  15. 15.
    Byers, J.A., Högberg, H.E., Unelius, C.R., Birgersson, G., Löfqvist, J.: Structure-activity studies on aggregation pheromone components of Pityogenes chalcographu. J. Chem. Ecol. 15, 685–695 (1989)CrossRefGoogle Scholar
  16. 16.
    Hill, C., Jones, T.M.: Stakeholder-agency theory. J. Manag. Stud. 29(2), 131–154 (1992)CrossRefGoogle Scholar
  17. 17.
    Vitense, H., Jacko, J., Emery, V.: Multimodal feedback: establishing a performance baseline for improved access by individuals with visual impairments, pp. 49–56 (2002)Google Scholar
  18. 18.
    Castillo-Garcia, J.: Interfaz cerebro computador adaptativa basada en agentes software para la discriminacion de cuatro tareas mentales. Doctoral theses, Universidad del valle (2015)Google Scholar
  19. 19.
    Bertrand, P.: A theoretical justification of the average reference in topographic evoked potential studies. Justification théorique de la référence moyenne dans les études topographiques de potentiels évoqués 62, 462–464 (1985)Google Scholar
  20. 20.
    Daubechies, I.: Ten Lectures of Wavelets, p. 357. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  21. 21.
    Trejo, L., Shensa, M.J.: Feature extraction of event-related potentials using wavelets: an application to human performance monitoring. Brain Lang. 66(1), 89–107 (1999)CrossRefGoogle Scholar
  22. 22.
    Jordan, M., Kleinberg, J., Schölkopf, B.: Pattern Recognition and Machine Learning (2017)Google Scholar
  23. 23.
    Bishop, C.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)zbMATHGoogle Scholar
  24. 24.
    Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math 20, 53–65 (1987)CrossRefGoogle Scholar
  25. 25.
    Castillo-Garcia, J., Hortal, E., Caicedo Bravo, E., Bastos, T., Azorin, J.: Feature selection based on Silhouette’s width for spontaneous brain computer interface. In: Conference: Proceedings of the 1st International Workshop on Assistive Technology, Vitoria - Brasil (2015)Google Scholar
  26. 26.
    Dubitzky, W., Granzow, M., Berrar, D.P.: Fundamentals of Data Mining in Genomics and Proteomics. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Duda, R.O., Stork, D.G., Hart, P.E.: Pattern Classification, p. 738 (2000)Google Scholar
  28. 28.
    Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)CrossRefGoogle Scholar
  29. 29.
    Roy, R.N., Charbonnier, S., Bonnet, S.: Detection of mental fatigue using an active BCI inspired signal processing chain. IFAC Proc. 19, 2963–2968 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Grupo de Investigación en Electrónica Industrial y Ambiental – GIEIAMUniversidad Santiago de CaliCaliColombia

Personalised recommendations