Abstract

This study addresses the issue of estimating mental efforts during a learning task based on brain signal measurements. The aim of this study is to describe how a Brain-Computer Interface (BCI) can be used as a direct communication tool between the human brain and a machine in the context of learning. Such devices are based on analyzing the electrical brain activity measurements using different brain exploration technics. In this study we describe an offline method to highlight effects of changing difficulty levels of a learning task on the prefrontal brain area status. Based on a single ElectroEncephaloGraphic (EEG) channel and using the Fisher-Snedecor test our proposed algorithm describes changes of Delta (0.5-3Hz), Theta (4-7Hz) and Alpha (8-11Hz) band powers. By using the Kappa coefficients, to assess the agreement rate between the algorithm decisions and those made by an expert, experimental results show a rate of 62% of agreement. This reflects the efficiency of the proposed method in distinguishing effects of difficulty levels of a learning task on the prefrontal brain area.

Keywords:

Brain waves, cognitive task, mental effort, mental status,

References

1. Basar, E., C. Basar-Eroglu, S. Karakas and M. Schürmann, 2001. Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int. J. Psychophysiol., 39(2-3): 241-248.
   CrossRef    Direct Link
2. Bell, C.J., P. Shenoy, R. Chalodhorn and R.P. Rao, 2008. Control of a humanoid robot by a noninvasive brain-computer interface in humans. J. Neural Eng., 5(2): 214-220.
   CrossRef    PMid:18483450    Direct Link
3. Conati, C. and C. Merten, 2007. Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation. Knowl-Based Syst., 20(6): 557-574.
   
4. Farwell, L.A. and E. Donchin, 1988. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neuro., 70(6): 510-523.
   
5. Holm, A., K. Lukander, J. Korpela, M. Sallinen and K.M. Müller, 2009. Estimating brain load from the EEG. Sci. World J., 9: 639-651.
   CrossRef    PMid:19618092    Direct Link
6. Kaida, K., T. Akerstedt, G. Kecklund, J.P. Nilsson and J. Axelsson, 2007. Use of Subjective and physiological indicators of sleepiness to predict performance during a vigilance task. Ind. Health, 45(4): 520-526.
   CrossRef    PMid:17878623    Direct Link
7. Klados, M.A., C. Papadelis, C. Braun and P.D. Bamidis, 2011. REG-ICA: A hybrid methodology combining blind source separation and regression techniques for the rejection of ocular artifacts. Biomed. Signal Proces., 6(3): 291-300.
   
8. Leeb, R., S. Perdikis, L. Tonin, A. Biasiucci, M. Tavella, M. Creatura, A. Molina, A. Al-Khodairy, T. Carlson and J.D. Millán, 2013. Transferring brain-computer interfaces beyond the laboratory: Successful application control for motor-disabled users. Artif. Intell. Med., 59(2): 121-132.
   CrossRef    PMid:24119870    Direct Link
9. Otmani, S., T. Pebayle, J. Roge and A. Muzet, 2005. Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers. Physiol. Behav., 84(5): 715-724.
   CrossRef    PMid:15885247    Direct Link
10. Papadelis, C., C. Kourtidou-Papadeli, P.D. Bamidis, I. Chouvarda, D. Koufogiannis, E. Bekiaris and N. Maglaveras, 2006. Indicators of sleepiness in an ambulatory EEG study of night driving. Proceeding of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06). NY, pp: 6201-6204.
    
11. Pires, G., U. Nunes and M. Castelo-Branco, 2012. Evaluation of brain-computer interfaces in accessing computer and other devices by people with severe motor impairments. Proc. Comput. Sci., 14: 283-292.
    
12. Rebolledo-Mendez, G., I. Dunwell, E.A. Martínez-Mirón, M.D. Vargas-Cerdán, S. de Freitas, F. Liarokapis and A.R. García-Gaona, 2009. Assessing NeuroSky's Usability to Detect Attention Levels in an Assessment Exercise. In: Jacko, J.A. (Ed.), Human-Computer Interaction. New Trends. Springer, Berlin, Heidelberg, pp: 149-158.
    CrossRef    Direct Link
13. Renard, Y., F. Lotte, G. Gibert, M. Congedo, E. Maby, V. Delannoy, O. Bertrand and A. Lécuyer, 2010. OpenViBE: An open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments. Presence, 19(1): 35-53.
    CrossRef    Direct Link
14. Wolpaw, J.R., N. Birbaumer, D.J. McFarland, G. Pfurtscheller and T.M. Vaughan, 2002. Brain-computer interfaces for communication and control. Clin. Neurophysiol., 113(6): 767-791.
    CrossRef    
15. Yasui, Y., 2009. A brainwave signal measurement and data processing technique for daily life applications. J. Physiol. Anthropol., 28(3): 145-150.
    CrossRef    PMid:19483376    Direct Link
16. Zammouri, A., A. Aitmoussa, S. Chevallier and E. Monacelli, 2015. Intelligentocular artifacts removal in a noninvasive singlechannel EEG recording. Proceeding of the IEEE Conference on Intelligent Systems and Computer Vision (ISCV, 2015). Fez, pp: 1-5.
    

Competing interests

The authors have no competing interests.

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