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A Shaping Procedure to Modulate Two Cognitive Tasks to Improve a Sensorimotor Rhythm-Based Brain-Computer Interface System

Published online by Cambridge University Press:  25 October 2018

Leandro da Silva-Sauer ,
Luis Valero-Aguayo ,
Francisco Velasco-Álvarez ,
Álvaro Fernández-Rodríguez  and
Ricardo Ron-Angevin
Leandro da Silva-Sauer*
Affiliation:
Universidade Federal da Paraíba (Brazil)
Luis Valero-Aguayo
Affiliation:
Universidad de Málaga (Spain)
Francisco Velasco-Álvarez
Affiliation:
Universidad de Málaga (Spain)
Álvaro Fernández-Rodríguez
Affiliation:
Universidad de Málaga (Spain)
Ricardo Ron-Angevin
Affiliation:
Universidad de Málaga (Spain)
*
*Correspondence concerning this article should be addressed to Leandro da Silva-Sauer. Laboratorio de Envelhecimento e Neurodegeneração, Departamento de Psicologia, Programa de Pós-Graduação em Neurociência Cognitiva e Comportamento, Cidade Universitária, Universidade Federal da Paraiba, s/n - Castelo Branco III, João Pessoa - PB, 58051-900. E-mail: [email protected]
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Abstract

This study aimed to propose an adapted feedback using a psychological learning technique based on Skinner’s shaping method to help the users to modulate two cognitive tasks (right-hand motor imagination and relaxed state) and improve better control in a Brain-Computer Interface. In the first experiment, a comparative study between performance in standard feedback (N = 9) and shaping method (N = 10) was conducted. The NASA Task Load Index questionnaire was applied to measure the user’s workload. In the second experiment, a single case study was performed (N = 5) to verify the continuous learning by the shaping method. The first experiment showed significant interaction effect between sessions and group (F(1, 17) = 5.565; p = .031) which the shaping paradigm was applied. A second interaction effect demonstrates a higher performance increase in the relax state task with shaping procedure (F(1, 17) = 5. 038; p = .038). In NASA-TXL an interaction effect was obtained between the group and the cognitive task in Mental Demand (F(1, 17) = 6, 809; p = .018), Performance (F(1, 17) = 5, 725; p = .029), and Frustration (F(1, 17) = 9, 735; p = .006), no significance was found in Effort. In the second experiment, a trial-by-trial analysis shows an ascendant trend learning curve for the cognitive task with the lowest initial acquisition (relax state). The results suggest the effectiveness of the shaping procedure to modulate brain rhythms, improving mainly the cognitive task with greater initial difficulty and provide better interaction perception.

Keywords

brain-computer interfacelearningsensory motor rhythmusshapingworkload
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 21 , 2018 , E44
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2018 

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Footnotes

This work was partially supported by the Spanish Ministerio de Economía y Competitividad through the projects LICOM (DPI2015-67064-R) and by the European Regional Development Fund (ERDF)

References

Barbero, Á., & Grosse-Wentrup, M. (2010). Biased feedback in brain-computer interfaces. Journal of NeuroEngineering and Rehabilitation, 7, 34. https://doi.org/10.1186/1743-0003-7-34CrossRefGoogle ScholarPubMed
Battison, A., Schlussel, M., Fuller, T., Yu, Y.-C., & Gabel, L. (2015, June). Effectiveness of subject specific instruction on mu-based brain-computer interface training. Paper presented at the 41st Annual Northeast Biomedical Engineering Conference (NEBEC), Troy, NY. https://doi.org/10.1109/NEBEC.2015.7117148Google Scholar
Birbaumer, N. (2006). Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control. Psychophysiology, 43(6), 517–532. https://doi.org/10.1111/j.1469–8986.2006.00456.xCrossRefGoogle ScholarPubMed
Burgers, C., Eden, A., van Engelenburg, M. D., & Buningh, S. (2015). How feedback boosts motivation and play in a brain-training game. Computers in Human Behavior, 48, 94–103. https://doi.org/10.1016/j.chb.2015.01.038CrossRefGoogle Scholar
Chaudhary, U., Birbaumer, N., & Ramos-Murguialday, A. (2016). Brain-computer interfaces for communication and rehabilitation. Nature Reviews Neurology, 12(9), 513–525. https://doi.org/10.1038/nrneurol.2016.113CrossRefGoogle ScholarPubMed
Chaudhary, U., Xia, B., Silvoni, S., Cohen, L. G., & Birbaumer, N. (2017). Brain-computer interface-based communication in the completely locked-in state. PLOS Biology, 15(1), e1002593. https://doi.org/10.1371/journal.pbio.1002593CrossRefGoogle ScholarPubMed
Corbit, V., Gabel, L. A., & Yu, Y. C. (2013, August). Improving mu rhythm brain-computer interface performance by providing specific instructions for control. Paper presented at the 41st Annual Northeast Biomedical Engineering Conference (NEBEC), Troy, NY. https://doi.org/10.1109/NEBEC.2013.166Google Scholar
da Silva-Sauer, L., Valero-Aguayo, L., Velasco-Álvarez, F., Ron-Angevin, R., & Sancha-Ros, S. (2013). Brain-computer interface: Proposal of a shaping-based training. Revista Brasileira de Engenharia Biomedica, 29(2), pp. 123–132. https://doi.org/10.4322/rbeb.2013.015CrossRefGoogle Scholar
da Silva-Sauer, L., Valero-Aguayo, L., Velasco-Álvarez, F., Varona-Moya, S., & Ron-Angevin, R. (2015). Training in realistic virtual environments: Impact on user performance in a motor imagery-based Brain–Computer Interface. In Rojas, I., Joya, G., & Catala, A. (Eds.), Advances in computational intelligence: Lecture notes in computer science (Vol. 9094, pp. 78–88). Cham, Switzerland: Springer.CrossRefGoogle Scholar
Da Silva-Sauer, L., Valero-Aguayo, L., de la Torre-Luque, A., Ron-Angevin, R., & Varona-Moya, S. (2016). Concentration on performance with P300–based BCI systems: A matter of interface features. Applied Ergonomics, 52, 325–332. https://doi.org/10.1016/j.apergo.2015.08.002CrossRefGoogle ScholarPubMed
Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., & Pfurtscheller, G. (2003). How many people are able to operate an EEG-based brain–computer interface (BCI). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11, 145–147. https://doi.org/10.1109/TNSRE.2003.814481CrossRefGoogle Scholar
Guger, C., Schlögl, G. A., Neuper, C., Walterspacher, D., Strein, T., & Pfurtscheller, G. (2001). Rapid prototyping of an EEG-based brain–computer interface (BCI). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9, 49–58. https://doi.org/10.1109/7333.918276CrossRefGoogle Scholar
Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Hancock, P. A. & Meshkati, N. (Eds.), Human mental workload (pp.139–183). Amsterdam, The Netherlands: North Holland Press.CrossRefGoogle Scholar
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487CrossRefGoogle Scholar
Jeunet, C., Jahanpour, E., & Lotte, F. (2016). Why standard brain-computer interface (BCI) training protocols should be changed: An experimental study. Journal of Neural Engineering, 13(3), 036024. https://doi.org/10.1088/1741-2560/13/3/036024CrossRefGoogle Scholar
Kübler, A., Neumann, N., Kaiser, J., Kotchoubey, B., Hinterberger, T., & Birbaumer, N. P. (2001). Brain computer communication: Self-regulation of slow cortical potentials for verbal communication. Archives of Physical Medicine and Rehabilitation, 82(11), 1533–1539. https://doi.org/10.1053/apmr.2001.26621CrossRefGoogle ScholarPubMed
Lakey, C. E., Berry, D. R., & Sellers, E. W. (2011). Manipulating attention via mindfulness induction improves P300–based brain-computer interface performance. Journal of Neural Engineering, 8(2), 025019. https://doi.org/10.1088/1741-2560/8/2/025019CrossRefGoogle ScholarPubMed
Li, T., Zhang, J., Xue, T., & Wang, B. (2017). Development of a novel motor imagery control technique and application in a gaming environment. Computational Intelligence and Neuroscience, 2017, ID 5863512. https://doi.org/10.1155/2017/5863512CrossRefGoogle Scholar
Lotte, F., Larrue, F., & Mühl, C. (2013). Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: Lessons learned from instructional design. Frontiers in Human Neuroscience, 7, 568. https://doi.org/10.3389/fnhum.2013.00568CrossRefGoogle ScholarPubMed
Lotte, F., & Jeunet, C. (2015, January). Towards improved BCI based on human learning principles. Paper presented at The 3rd International Winter Conference on Brain-Computer Interface, Sabuk, 2015, pp. 1–4. https://doi.org/10.1109/IWW-BCI.2015.7073024Google Scholar
Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326. https://doi.org/10.1007/s10648-007-9047-2CrossRefGoogle Scholar
Neuper, C., & Pfurtscheller, G. (1999). Motor imagery and event-related desynchronization. In Pfurtscheller, G. & da Silva, F. H. L. (Eds.), Handbook of electroencephalography and clinical neurophysiology , (Rev. series, pp. 303–325). Amsterdam, The Netherlands: Elseiver.Google Scholar
Neuper, C., & Pfurtscheller, G. (2010). Neurofeedback training for BCI control. In Graimann, B., Pfurtscheller, G., & Allison, B. (Eds.), Brain-Computer Interfaces (pp. 65–78). London, UK: Springer.Google Scholar
Nijboer, F., Birbaumer, N., & Kübler, A. (2010). The influence of psychological state and motivation on Brain-Computer interface performance in patients with amyotrophic lateral sclerosis: A longitudinal study. Frontiers in Neuroscience, 4. https://doi.org/10.3389/fnins.2010.00055Google ScholarPubMed
Özdenizci, O., Yalçın, M., Erdoğan, A., Patoğlu, V., Grosse-Wentrup, M., & Çetin, M. (2017). Electroencephalographic identifiers of motor adaptation learning. Journal of Neural Engineering, 14, 046027. https://doi.org/10.1088/1741-2552/aa6abdCrossRefGoogle ScholarPubMed
Ron-Angevin, R., & Díaz-Estrella, A. (2009). Brain–computer interface: Changes in performance using virtual reality techniques. Neuroscience Letters, 449(2), 123–127. https://doi.org/10.1016/j.neulet.2008.10.099CrossRefGoogle ScholarPubMed
Ron-Angevin, R., Velasco-Álvarez, F., Fernández-Rodríguez. Á., , Díaz-Estrella, A., Blanca-Mena, M. J., & Vizcaíno-Martín, F. J. (2017). Brain-Computer Interface application: Auditory serial interface to control a two-class motor-imagery-based wheelchair. Journal of NeuroEngineering and Rehabilitation, 14(1), 49. https://doi.org/10.1186/s12984-017-0261-yCrossRefGoogle ScholarPubMed
Skinner, B. F. (1969). Contingencies of reinforcement: A theoretical analysis. New York, NY: Appleton-Century-Crofts.Google Scholar
Sollfrank, T., Ramsay, A., Perdikis, S., Williamson, J., Murray-Smith, R., Leeb, R., ... Kübler, A. (2016). The effect of multimodal and enriched feedback on SMR-BCI performance. Clinical Neurophysiology , 127(1), 490–498. https://doi.org/10.1016/j.clinph.2015.06.004CrossRefGoogle ScholarPubMed
Tan, L.-F., Dienes, Z., Jansari, A., & Goh, S.-Y. (2014). Effect of mindfulness meditation on brain–computer interface performance. Consciousness and Cognition, 23, 12–21. https://doi.org/10.1016/j.concog.2013.10.010CrossRefGoogle ScholarPubMed
Velasco-Álvarez, F., Ron-Angevin, R., & Blanca-Mena, M. J. (2010). Free virtual navigation using motor imagery through an asynchronous brain-computer interface. Presence: Teleoperators and Virtual Environments 19(1), 71–81. https://doi.org/10.1162/pres.19.1.71CrossRefGoogle Scholar
Velasco-Álvarez, F., Ron-Angevin, R., da Silva-Sauer, L., & Sancha-Ros, S. (2013). Audio-cued motor imagery-based brain–computer interface: Navigation through virtual and real environments. Neurocomputing, 121, 89–98. https://doi.org/10.1016/j.neucom.2012.11.038CrossRefGoogle Scholar