Assessing NeuroSky’s Usability to Detect Attention Levels in an Assessment Exercise

  • Genaro Rebolledo-Mendez
  • Ian Dunwell
  • Erika A. Martínez-Mirón
  • María Dolores Vargas-Cerdán
  • Sara de Freitas
  • Fotis Liarokapis
  • Alma R. García-Gaona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5610)

Abstract

This paper presents the results of a usability evaluation of the NeuroSky’s MindSet (MS). Until recently most Brain Computer Interfaces (BCI) have been designed for clinical and research purposes partly due to their size and complexity. However, a new generation of consumer-oriented BCI has appeared for the video game industry. The MS, a headset with a single electrode, is based on electro-encephalogram readings (EEG) capturing faint electrical signals generated by neural activity. The electrical signal across the electrode is measured to determine levels of attention (based on Alpha waveforms) and then translated into binary data. This paper presents the results of an evaluation to assess the usability of the MS by defining a model of attention to fuse attention signals with user-generated data in a Second Life assessment exercise. The results of this evaluation suggest that the MS provides accurate readings regarding attention, since there is a positive correlation between measured and self-reported attention levels. The results also suggest there are some usability and technical problems with its operation. Future research is presented consisting of the definition a standardized reading methodology and an algorithm to level out the natural fluctuation of users’ attention levels if they are to be used as inputs.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Amershi, S., Conati, C., McLaren, H.: Using Feature Selection and Unsupervised Clustering to Identify Affective Expressions in Educational Games. In: Workshop in Motivational and Affective Issues in ITS, 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan (2006)Google Scholar
  2. 2.
    Association, A.P.: Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Press (1994)Google Scholar
  3. 3.
    Berger, H.: On the electroencephalogram of man. In: Gloor, P. (ed.) The fourteen original reports on the human electroencephalogram, Amsterdam (1969)Google Scholar
  4. 4.
    Haynes, J.D., Rees, G.: Decoding mental states from brain activity in humans. Nature Neuroscience 7(7) (2006)Google Scholar
  5. 5.
    Linden, M., Habib, T., Radojevic, V.: A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Applied Psychophysiology and Biofeedback 21(1) (1996)Google Scholar
  6. 6.
    Loudin, J.D., et al.: Optoelectronic retinal prosthesis: system design and performance. Journal of Neural Engineering 4, 72–84 (2007)CrossRefGoogle Scholar
  7. 7.
    Manske, M., Conati, C.: Modelling Learning in an Educational Game. In: 12th Conference on Artificial Intelligence in Education, IOS Press, Amsterdam (2005)Google Scholar
  8. 8.
    Mason, S.G., Birch, G.E.: A general framework for brain-computer interface design. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 70–85 (2003)CrossRefGoogle Scholar
  9. 9.
    Poli, R., Cinel, C., Citi, L., Sepulveda, F.: Evolutionary brain computer interfaces. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 301–310. Springer, Heidelberg (2007)Google Scholar
  10. 10.
    Rebolledo-Mendez, G., De Freitas, S.: Attention modeling using inputs from a Brain Computer Interface and user-generated data in Second Life. In: The Tenth International Conference on Multimodal Interfaces (ICMI 2008), Crete, Greece (2008)Google Scholar
  11. 11.
    Sona, D., Veeramachaneni, S., Olivetti, E., Avesani, P.: Inferring cognition from fMRI brain images. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 869–878. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Sitaram, R., et al.: fMRI Brain-Computer Interfaces. IEEE Signal Processing Magazine 25(1), 95–106 (2008)CrossRefGoogle Scholar
  13. 13.
    Trejo, L.J., Rosipal, R., Matthews, B.: Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2), 225–229 (2006)CrossRefGoogle Scholar
  14. 14.
    Vaughan, T., et al.: Brain-computer interface technology: a review of the second international meeting. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2), 94–109 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Genaro Rebolledo-Mendez
    • 1
    • 3
  • Ian Dunwell
    • 1
  • Erika A. Martínez-Mirón
    • 2
  • María Dolores Vargas-Cerdán
    • 3
  • Sara de Freitas
    • 1
  • Fotis Liarokapis
    • 4
  • Alma R. García-Gaona
    • 3
  1. 1.Serious Games InstituteCoventry UniversityUK
  2. 2.CCADET, UNAMMexico
  3. 3.Facultad de Estadistica e InformaticaUniversidad VeracruzanaMexico
  4. 4.Interactive Worlds Applied Research GroupCoventry UniversityUK

Personalised recommendations