Gaming the Attention with a SSVEP-Based Brain-Computer Interface
Steady-State Visually Evoked Potentials (SSVEPs) have been widely used in neuroscience for the characterization of dynamic processes from the retina to the visual cortex. In Neuro-engineering, SSVEP-based Brain-computer Interfaces (SSVEP-BCIs) have been used in variety of applications (e. g., communication, entertainment, etc.) for the detection of attention to visual stimuli. In this work, we propose a hands-free videogame in which the player joystick is a SSVEP-BCI. In the videogame, hostile avatars fire weapons against the player who could deflect them if enough attention is exerted. Attention is detected based on the analysis of SSVEP and Alphaband powers. For this purpose, weapons are mobile checkerboards that flicker at a constant frequency. We presented this videogame as a demo in a technologic event for students of engineering who freely tried it. The main findings were: (i) the attention detection algorithm based on SSVEPs is robust enough to be performed in few seconds even with mobile visual stimuli and in a non-isolated room; (ii) the videogame is capable to dose and quantify the amount of cognitive attention that a player exerts on mobile stimuli by controlling their time and position. The results suggest that this videogame could be used as a serious game to play/train the attentional and visual tracking capabilities with direct application in Special Needs Education or in attention disorders.
KeywordsAttention SSVEP Gamification EEG Brain-computer Interface
This research was funded by the Ministry of Economy and Competitiveness (Spain) grant number [TIN2015-67020P], the Junta of Andalucia (Spain) grant number [P11-TIC-7983], the Spanish National Youth Guarantee Implementation Plan, the Association Nicolo for the R+D in Neurotechnologies for the disability. The authors would like to thank all the volunteers who participated in the study, participants of the University of Granada Lan Party (ULP) 2018 and the I Workshop of Telecommunication Engineering at the ETSIT of the University of Granada.
- 17.Pires, G., Torres, M., Casaleiro, N., Nunes, U., Castelo-Branco, M.: Playing Tetris with non-invasive BCI, pp. 1–6. IEEE (2011). https://doi.org/10.1109/SeGAH.2011.6165454
- 21.BCI Lab—Universidad de Granada. Available atGoogle Scholar
- 22.Klem, G.H., Lüders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol. 52(Suppl. 3), 3–6 (1999). The International Federation of Clinical NeurophysiologyGoogle Scholar
- 23.Lopez, M.A., Pomares, H., Damas, M., Prieto, A., de la Plaza Hernandez, E.M.: Use of Kohonen maps as feature selector for selective attention brain-computer interfaces. In: Mira, J., Álvarez, J.R. (eds.) Bio-inspired Modeling of Cognitive Tasks. LNCS, vol. 4527, pp. 407–415. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73053-8_41CrossRefGoogle Scholar