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EEG-based BCI and video games: a progress report

  • S.I. : VR and AR Serious Games
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Abstract

This paper presents a systematic review of electroencephalography (EEG)-based brain–computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.

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References

  • Ahn M, Cho H et al (2013) High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PloS ONE 8(11):e80886

    Article  Google Scholar 

  • Ahn M, Jun SC (2015) Performance variation in motor imagery brain-computer interface: a brief review. J Neurosci Methods 243:103–110

    Article  Google Scholar 

  • Ahn M, Lee M et al (2014) A review of brain-computer interface games and an opinion survey from researchers, developers and users. Sensors 14(8):14601–14633

    Article  Google Scholar 

  • Ali A, Puthusserypady S (2015) A 3D learning playground for potential attention training in ADHD: a brain computer interface approach. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 67–70

  • Allison BZ et al (2010) Toward a hybrid brain-computer interface based on imagined movement and visual attention. J Neural Eng 7(2):026007

    Article  Google Scholar 

  • Allison B et al (2010) BCI demographics: how many (and what kinds of) people can use an SSVEP BCI? IEEE Trans Neural Syst Rehabil Eng 18(2):107–116

    Article  Google Scholar 

  • Amaral CP, Simões MA, Castelo-Branco MS (2015) Neural signals evoked by stimuli of increasing social scene complexity are detectable at the single-trial level and right lateralized. PloS ONE 10(3):e0121970

    Article  Google Scholar 

  • Ang KK et al (2015) A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci 46(4):310–320

    Article  Google Scholar 

  • Angeloni C et al (2012) P300-based brain–computer interface memory game to improve motivation and performance. In: 2012 38th annual northeast bioengineering conference (NEBEC). IEEE, pp 35–36

  • Bai O et al (2007) A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior. J Neural Eng 5(1):24

    Article  Google Scholar 

  • Banville H, Falk TH (2016) Recent advances and open challenges in hybrid brain-computer interfacing: a technological review of non-invasive human research. Brain Comput Interfaces 3(1):9–46

    Article  Google Scholar 

  • Bayliss JD, Ballard DH (2000) Single trial P3 epoch recognition in a virtual environment. Neurocomputing 32:637–642

    Article  Google Scholar 

  • Berger TW et al (2008) Brain-computer interfaces: an international assessment of research and development trends. Springer, Berlin

    Book  Google Scholar 

  • Bernays R et al (2012) Lost in the dark: emotion adaption. In: Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology. ACM, pp 79–80

  • Berta R et al (2013) Electroencephalogram and physiological signal analysis for assessing flow in games. IEEE Trans Comput Intell AI Games 5(2):164–175

    Article  Google Scholar 

  • Beveridge R, Wilson S, Coyle D (2016) 3D graphics, virtual reality, and motion-onset visual evoked potentials in neurogaming. Progr Brain Res 228:329–353

    Article  Google Scholar 

  • Bianchi L et al (2010) Which physiological components are more suitable for visual erp based brain-computer interface? A preliminary MED/EEG study. Brain Topogr 23(2):180–185

    Article  Google Scholar 

  • Bin G et al (2009) VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier]. IEEE Comput Intell Mag 4(4):22–26

    Article  Google Scholar 

  • Blankertz B, Sannelli C et al (2010) Neurophysiological predictor of SMR-based BCI performance. Neuroimage 51(4):1303–1309

    Article  Google Scholar 

  • Blankertz B, Sanelli C et al (2009) Predicting BCI performance to study BCI illiteracy. BMC Neuroscience 10(Suppl 1):P84. doi:10.1186/1471-2202-10-S1-P84

    Article  Google Scholar 

  • Bonnet L, Lotte F, Lécuyer A (2013) Two brains, one game: design and evaluation of a multiuser BCI video game based on motor imagery. IEEE Trans Comput Intell AI Games 5(2):185–198

    Article  Google Scholar 

  • Bordoloi S, Sharmah U, Hazarika SM (2012) Motor imagery based BCI for a maze game. In: 2012 4th international conference on intelligent human computer interaction (IHCI). IEEE, pp 1–6

  • Bos DP-O et al (2010) Brain-computer interfacing and games. In: Bos DP-O, Boris R (eds) Brain-computer interfaces. Springer, London, pp 149–178

    Google Scholar 

  • Carofiglio V, Abbattista F (2013) A rough BCI-based assessment of user’s emotions for interface adaptation: Application to a 3D-virtual-environment exploration task. In: Ai* Hci@ Ai* Ia

  • Causse M et al (2015) EEG/ERP as a measure of mental workload in a simple piloting task. Proc Manuf 3:5230–5236

    Google Scholar 

  • Cernea D et al (2013) Emotion scents: a method of representing user emotions on gui widgets. In: IS&T/SPIE electronic imaging. International Society for Optics and Photonics, pp 86540F–86540F

  • Chouhan T et al (2015) A comparative study on the effect of audio and visual stimuli for enhancing attention and memory in brain computer interface system. In: 2015 IEEE International Conference on systems, man, and cybernetics (SMC). IEEE, pp 3104–3109

  • Chumerin N et al (2013) Steady-state visual evoked potential-based computer gaming on a consumer-grade EEG device. IEEE Transa Comput Intell AI Games 5(2):100–110

    Article  Google Scholar 

  • Cohen A et al (2016) Multi-modal virtual scenario enhances neurofeedback learning. Front Robot AI 3:52

    Article  Google Scholar 

  • Collinger JL et al (2013) High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381(9866):557–564

    Article  Google Scholar 

  • Congedo M et al (2011) “ Brain invaders”: a prototype of an open-source P300-based video game working with the OpenVIBE platform. In: 5th international brain–computer interface conference 2011 (BCI 2011), pp 280–283

  • Coulton P, Wylie CG, Bamford W (2011) Brain interaction for mobile games. In: Proceedings of the 15th international academic MindTrek conference: envisioning future media environments. ACM, pp 37–44

  • Coyle D, Garcia J et al (2011) EEG-based continuous control of a game using a 3 channel motor imagery BCI: BCI game. In: 2011 IEEE symposium on computational intelligence, cognitive algorithms, mind, and brain (CCMB). IEEE, pp 1–7

  • Coyle D, Stow J et al (2015) Action games, motor imagery, and control strategies: toward a multi-button controller. In: Nakatsu R, Rauterberg M, Ciancarini P (eds) Handbook of digital games and entertainment technologies, Springer, Singapore

    Google Scholar 

  • Daly JJ, Cheng R et al (2009) Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther 33(4):203–211

    Article  Google Scholar 

  • Daly JJ, Wolpaw JR (2008) Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 7(11):1032–1043

    Article  Google Scholar 

  • de Lissa P et al (2015) Measuring the face-sensitive N170 with a gaming EEG system: a validation study. J Neurosci Methods 253:47–54

    Article  Google Scholar 

  • De Vos M, Gandras K, Debener S (2014) Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away. Int J Psychophysiol 91(1):46–53

    Article  Google Scholar 

  • Edlinger G, Güger C (2011) Social environments, mixed communication and goal-oriented control application using a brain–computer interface. In: Edlinger G, Güger C (eds) Universal access in human–computer interaction. Users diversity, pp 545–554

  • Enriquez-Geppert S, Huster RJ, Herrmann CS (2017) EEG-neurofeedback as a tool to modulate cognition and behavior: a review tutorial. Front Hum Neurosci 11:1–51

    Article  Google Scholar 

  • Ewing KC, Fairclough SH, Gilleade K (2016) Evaluation of an adaptive game that uses EEG measures validated during the design process as inputs to a biocybernetic loop. Front Hum Neurosci 10:223. doi:10.3389/fnhum.2016.00223

    Article  Google Scholar 

  • Falkenstein M et al (2000) ERP components on reaction errors and their functional significance: a tutorial. Biol Psychol 51(2):87–107

    Article  Google Scholar 

  • Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523

    Article  Google Scholar 

  • Ferreira ALS, Marciano JN et al (2014) Understanding and proposing a design rationale of digital games based on brain-computer interface: Results of the admiralmind battleship study. SBC J Interact Syst 5(1):3–15

    Google Scholar 

  • Ferreira ALS, de Miranda LC (2013) A survey of interactive systems based on brain-computer interfaces. SBC J Interact Syst 4(1):3–13

    Google Scholar 

  • Finke A, Lenhardt A, Ritter H (2009) The MindGame: a P300-based brain-computer interface game. Neural Netw 22(9):1329–1333

    Article  Google Scholar 

  • Fouad MM et al (2015) Brain computer interface: a review. In: Fouad MM, Amin KM, El-Bendary N, Hassanien AE (eds) Brain-computer interfaces. Springer, Berlin, pp 3–30

    Google Scholar 

  • Frey J et al (2013) Review of the use of electroencephalography as an evaluation method for human–computer interaction. arXiv preprint arXiv:1311.2222

  • Friedrich EVC et al (2014) Brain–computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum. Front Neuroeng 7:21

    Article  Google Scholar 

  • Ganin IP, Shishkin SL, Kaplan AY (2011) A P300 BCI with stimuli presented on moving objects. na

  • Gilroy SW et al (2013) A brain–computer interface to a plan-based narrative. In: IJCAI, pp 633–2

  • Guger C et al (2003) How many people are able to operate an EEG-based brain-computer interface (BCI)? IEEE Trans Neural Syst Rehabil Eng 11(2):145–147

    Article  Google Scholar 

  • Guger C, Daban S et al (2009) How many people are able to control a P300-based brain-computer interface (BCI)? Neurosci Lett 462(1):94–98

    Article  Google Scholar 

  • Guger C, Allison B, Müller-Putz G (2015) Recent advances in brain-computer interface research–a summary of the BCI Award 2014 and BCI research trends. Brain-computer interface research. Springer, Berlin, pp 127–133

    Chapter  Google Scholar 

  • Guo F et al (2008) A brain-computer interface using motion-onset visual evoked potential. J Neural Eng 5(4):477

    Article  Google Scholar 

  • Gürkök H, van de Laar B et al (2014) Players’ opinions on control and playability of a BCI game. International conference on universal access in human-computer interaction. Springer, Berlin, pp 549–560

    Google Scholar 

  • Gürkök H, Nijholt A, Poel M (2012) Brain-computer interface games: towards a framework. Entertainment computing-ICEC 2012:373–380

    Google Scholar 

  • Gurkok H, Nijholt A (2013) Affective brain–computer interfaces for arts. In: 2013 humaine association conference on affective computing and intelligent interaction (ACII). IEEE, pp 827–831

  • Hakvoort G et al (2011) Measuring immersion and affect in a brain-computer interface game. Human-computer interaction-INTERACT 2011:115–128

    Google Scholar 

  • Hasan BAS, Gan JQ (2012) Hangman BCI: An unsupervised adaptive self-paced brain-computer interface for playing games. Comput Biol Med 42(5):598–606

    Article  Google Scholar 

  • Heidrich R de O et al (2015) Development of BCI based softwares to assist people with mobility limitations in the school inclusion process. In: Proceedings of the 17th international ACM SIGACCESS conference on computers & accessibility. ACM, pp 397–398

  • Huster RJ et al (2014) Brain-computer interfaces for EEG neurofeedback: Peculiarities and solutions. Int J Psychophysiol 91(1):36–45

    Article  Google Scholar 

  • Hwang H-J et al (2013) EEG-based brain-computer interfaces: a thorough literature survey. Int J Hum Comput Interact 29(12):814–826

    Article  Google Scholar 

  • i Badia SB et al (2011) Exploring the synergies of a hybrid BCI-VR neurorehabilitation system. In: 2011 international conference on virtual rehabilitation (ICVR). IEEE, pp 1–8

  • J Del R Millán (2008) Non-invasive brain-machine interaction. Int J Pattern Recognit Artif Intell 22(05):959–972

    Article  Google Scholar 

  • Järvelä S et al (2015) Stimulus games. In: Lankoski P, Björk S (eds) Game research methods. ETC Press, Pittsburgh, pp 193–205. ISBN 978-1-312-88473-1. http://dl.acm.org/citation.cfm?id=2812774.2812790

  • Jasper H (1958) Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr Clin Neurophysiol 10:370–375

    Article  Google Scholar 

  • Jeunet C, Jahanpour E, Lotte F (2016) Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study. J Neural Eng 13(3):036024

    Article  Google Scholar 

  • Jin J et al (2014) An optimized ERP brain-computer interface based on facial expression changes. J Neural Eng 11(3):036004

    Article  Google Scholar 

  • Joselli M et al (2014) MindNinja: concept, development and evaluation of a mind action game based on EEGs. In: 2014 Brazilian symposium on computer games and digital entertainment (SBGAMES). IEEE, pp 123–132

  • Jurcak V, Tsuzuki D, Dan I (2007) 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. NeuroImage 34(4):1600–1611

    Article  Google Scholar 

  • Kapeller C, Hintermüller C, Guger C (2012) Augmented control of an avatar using an SSVEP based BCI. In: Proceedings of the 3rd augmented human international conference. ACM, p 27

  • Kaplan AY et al (2013) Adapting the P300-based brain-computer interface for gaming: a review. IEEE Trans Comput Intell AI Games 5(2):141–149

    Article  Google Scholar 

  • Kaufmann T et al (2011) Flashing characters with famous faces improves ERP-based brain-computer interface performance. J Neural Eng 8(5):056016

    Article  Google Scholar 

  • Kerous B, Liarokapis F (2016) Brain–computer interfaces—a survey on interactive virtual environments. In: 2016 8th international conference on games and virtual worlds for serious applications (VS-games). IEEE, pp 1–4

  • Khong A et al (2014) BCI based multi-player 3-D game control using EEG for enhancing attention and memory. In: 2014 IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 1847–1852

  • Kleber B, Birbaumer N (2005) Direct brain communication: neuroelectric and metabolic approaches at Tübingen. Cogn Process 6(1):65–74

    Article  Google Scholar 

  • Ko M et al (2009) A study on new gameplay based on brain–computer interface. In: Proceedings of DiGRA

  • Koo B et al (2015) Immersive BCI with SSVEP in VR head-mounted display. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 1103–1106

  • Kos’ Myna N, Tarpin-Bernard F (2013) Evaluation and comparison of a multimodal combination of BCI paradigms and eye tracking with affordable consumer-grade hardware in a gaming context. IEEE Trans Comput Intell AI Games 5(2):150–154

    Article  Google Scholar 

  • Kotsia I, Zafeiriou, S, Fotopoulos S (2013) Affective gaming: A comprehensive survey. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 663–670

  • Kreilinger A et al (2011) Switching between manual control and brain–computer interface using long term and short term quality measures. Front Neurosci 5:147. doi:10.3389/fnins.2011.00147

    Google Scholar 

  • Krepki R et al (2007) The berlin brain-computer interface (BBCI)-towards a new communication channel for online control in gaming applications. Multimed Tools Appl 33(1):73–90

    Article  Google Scholar 

  • Kuba M, Kubová Z (1992) Visual evoked potentials specific for motion onset. Doc Ophthalmol 80(1):83–89

    Article  Google Scholar 

  • Kübler A et al (2005) Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64(10):1775–1777

    Article  Google Scholar 

  • Lécuyer A et al (2008) Brain-computer interfaces, virtual reality, and videogames. Computer 41(10):66–72

    Article  Google Scholar 

  • Leeb R, Lancelle M et al (2013) Thinking penguin: Multimodal brain-computer interface control of a VR game. IEEE Trans Comput Intell AI Games 5(2):117–128

    Article  Google Scholar 

  • Leeb R, Friedman D, et al (2007) Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci 2007:79642. doi:10.1155/2007/79642

    Article  Google Scholar 

  • Legény J, Viciana-Abad R, Lécuyer A (2013) Toward contextual SSVEP-based BCI controller: smart activation of stimuli and control weighting. IEEE Trans Comput Intell AI Games 5(2):111–116

    Article  Google Scholar 

  • Li J et al (2013) A competitive brain computer interface: Multi-person car racing system. In: 2013 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 2200–2203

  • Liao L-D et al (2012) Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil 9(1):5

    Article  Google Scholar 

  • Liarokapis F, Vourvopoulos A, Ene A (2015) Examining user experiences through a multimodal BCI puzzle game. In: 2015 19th international conference on information visualisation (iV). IEEE, pp 488–493

  • Lim WL, Sourina O, Wang L (2015) Mind-an EEG neurofeedback multitasking game. In: 2015 International conference on cyberworlds (CW). IEEE, pp 169–172

  • Liu Y, Sourina O, Hou X (2014) Neurofeedback games to improve cognitive abilities. In: 2014 international conference on cyberworlds (CW). IEEE, pp 161–168

  • Lopetegui E, Zapirain BG, Mendez A (2011) Tennis computer game with brain control using EEG signals. In: 2011 16th international conference on computer games (CGAMES). IEEE, pp 228–234

  • Lotte F (2011) Brain–computer interfaces for 3D games: hype or hope? In: Proceedings of the 6th international conference on foundations of digital games. ACM, pp 325–327

  • Lotte F et al (2012) Combining BCI with virtual reality: towards new applications and improved BCI. In: Lotte F, Faller J, Guger C, Renard Y, Pfurtscheller G, Lécuyer A, Leeb R (eds) Towards practical brain-computer interfaces. Springer, Berlin, pp 197–220

    Chapter  Google Scholar 

  • Loup-Escande E et al (2017) User-centered BCI videogame design. In: Emilie L-E, Lotte F, Loup G, Lécuyer A (eds) Handbook of digital games and entertainment technologies, pp 225–250

  • Luck SJ (2004) Ten simple rules for designing and interpreting ERP experiments. In: Handy, TC (ed) Event-related potentials: a methods handbook, A Bradford book, MIT Press,

  • Luck SJ (2014) An introduction to the event-related potential technique. MIT Press, Boca Raton

    Google Scholar 

  • Maby E et al (2012) BCI could make old two-player games even more fun: a proof of concept with connect four. Adv Hum Comput Interact 2012:1

    Article  Google Scholar 

  • Marshall D, Coyle D et al (2013) Games, gameplay, and BCI: the state of the art. IEEE Trans Comput Intell AI Games 5(2):82–99

    Article  Google Scholar 

  • Marshall D, Beveridge R et al (2015) Interacting with multiple game genres using motion onset visual evoked potentials. In: Computer games: AI, animation, mobile, multimedia, educational and serious games (CGAMES), 2015. IEEE, pp 18–27

  • Marshall D, Wilson S, Coyle D (2015) Motion-onset visual evoked potentials for gaming. In: Proceedings of the international conference on computer games, multimedia & allied technology (CGAT). Global Science and Technology Forum, p 155

  • Martinez P, Bakardjian H, Cichocki A (2007) Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm. Computat Intell Neurosci 2007:13–13

    Google Scholar 

  • Mayer K, Wyckoff SN, Strehl U (2013) One size fits all? slow cortical potentials neurofeedback: a review. J Atten Disord 17(5):393–409

    Article  Google Scholar 

  • McCreadie KA, Coyle DH, Prasad G (2012) Sensorimotor-rhythm modulation feedback with 3D vector-base amplitude panning—a brain–computer interface pilot study. In: IET Irish Signals and Systems Conference (ISSC 2012), 28–29 June 2012, Maynooth, Ireland

  • McMahan T, Parberry I, Parsons TD (2015a) Evaluating player task engagement and arousal using electroencephalography. Proc Manuf 3:2303–2310

    Google Scholar 

  • McMahan T, Parberry I, Parsons TD (2015b) Modality specific assessment of video game player’s experience using the Emotiv. Entertain Comput 7:1–6

    Article  Google Scholar 

  • Mercier-Ganady J et al (2013) Can we use a brain–computer interface and manipulate a mouse at the same time? In: Proceedings of the 19th ACM symposium on virtual reality software and technology. ACM, pp 69–72

  • Mondéjar T et al (2016) Correlation between videogame mechanics and executive functions through EEG analysis. J Biomed Inform 63:131–140

    Article  Google Scholar 

  • Moore Jackson M et al (2009) Continuous control paradigms for direct brain interfaces. Human–computer interaction. Novel interaction methods and techniques, pp 588–595

  • Mühl C, Allison B et al (2014) A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges. Brain Comput Interfaces 1(2):66–84

    Article  Google Scholar 

  • Mühl C, Gürkök H et al (2010) Bacteria hunt: a multimodal, multiparadigm BCI game. In: Mühl C, Gürkök H, Bos DP-O, Thurlings ME, Scherffig L, Duvinage M, Elbakyan AA, Kang SW, Poel M, Heylen D Workshop report for the enterface workshop in Genova, Italy 2009

  • Mulholland T (1995) Human EEG, behavioral stillness and biofeedback. Int J Psychophysiol 19(3):263–279

    Article  Google Scholar 

  • Müller-Putz G, Scherer R, Pfurtscheller G (2007) Game-like training to learn single switch operated neuroprosthetic control. In: Proceedings of BRAINPLAY 2007, playing with your brain

  • Muñoz JE, Chavarriaga R, Lopez DS (2014) Application of hybrid BCI and exergames for balance rehabilitation after stroke. In: Proceedings of the 11th conference on advances in computer entertainment technology. ACM, p 67

  • Muñoz JE, Lopez DS et al (2015) Design and creation of a BCI videogame to train sustained attention in children with adhd. In: 2015 10th computing Colombian conference (10CCC). IEEE, pp 194–199

  • Nacke LE (2015) Games user research and physiological game evaluation. In: Nacke LE (eds) Game user experience evaluation. Springer, Berlin, pp 63–86

    Chapter  Google Scholar 

  • Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12(2):1211–1279

    Article  Google Scholar 

  • Nijboer F et al (2013) The asilomar survey: stakeholders’ opinions on ethical issues related to brain-computer interfacing. Neuroethics 6(3):541–578

    Article  Google Scholar 

  • Oude Bos D, Reuderink B (2008) Brainbasher: a BCI game. Eindhoven University of Technology, Eindhoven, Netherlands

  • Palke A (2004) Master thesis: Brainathlon: enhancing brainwave control through brain-controlled game play. PhD thesis, Mills College

  • Parafita R et al (2013) A spacecraft game controlled with a brain–computer interface using SSVEP with phase tagging. In: 2013 IEEE 2nd international conference on serious games and applications for health (SeGAH). IEEE, pp 1–6

  • Parenthoen M, Murie F, Thery F (2015) The sea is your mirror. In: Proceedings of the 8th ACM SIGGRAPH conference on motion in games. ACM, pp 159–165

  • Park K et al (2016) Narratives and sensor driven cognitive behavior training game platform. In: 2016 IEEE 14th international conference on software engineering research, management and applications (SERA). IEEE, pp 125–131

  • Picton T (1990) Human brain electrophysiology. Evoked potentials and evoked magnetic fields in science and medicine. J Clin Neurophysiol 7(3):450–451

    Article  Google Scholar 

  • Pike M et al (2016) \(\#\) scanners: exploring the control of adaptive films using brain–computer interaction. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, pp 5385–5396

  • Pineda JA et al (2003) Learning to control brain rhythms: making a brain-computer interface possible. IEEE Trans Neural Syst Rehabil Eng 11(2):181–184

    Article  Google Scholar 

  • Pires G et al (2011) Playing tetris with non-invasive BCI. In: 2011 IEEE 1st international conference on serious games and applications for health (SeGAH). IEEE, pp 1–6

  • Po-Lei L, Hao-Teng S, Hsiang-Chih C (2014) Design a brain computer interface gaming system using steady-state visual evoked potential. In: 2014 IEEE international conference on consumer electronics-Taiwan (ICCE-TW). IEEE, pp 5–6

  • Ramadan RA et al (2015) Basics of brain computer interface. In: Ramadan RA, Refat S, Elshahed MA, Ali RA (eds) Brain-computer interfaces. Springer, Berlin, pp 31–50

    Google Scholar 

  • Riechmann H, Finke A, Ritter H (2016) Using a cVEP-based brain-computer interface to control a virtual agent. IEEE Trans Neural Syst Rehabil Eng 24(6):692–699

    Article  Google Scholar 

  • Rohani DA, Puthusserypady S (2015) BCI inside a virtual reality classroom: a potential training tool for attention. EPJ Nonlinear Biomed Phys 3(1):12

    Article  Google Scholar 

  • Ryan RM, Deci EL (2000) Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol 55(1):68

    Article  Google Scholar 

  • Scherer R, Friedrich EC et al (2011) Non-invasive brain-computer interfaces: Enhanced gaming and robotic control. International work-conference on artificial neural networks. Springer, Berlin, pp 362–369

    Google Scholar 

  • Scherer R, Schloegl A et al (2007) The self-paced Graz brain–computer interface: methods and applications. In: Scherer R, Schloegl A, Lee F, Bischof H, Janša J, Pfurtscheller G (eds) Computational intelligence and neuroscience

  • Sellers EW, Vaughan TM, Wolpaw JR (2010) A brain-computer interface for long-term independent home use. Amyotroph lateral Scler 11(5):449–455

    Article  Google Scholar 

  • Shenjie S, Thomas KP, Vinod A et al (2014) Two player EEG-based neurofeedback ball game for attention enhancement. In: 2014 IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 3150–3155

  • Shim B-S, Lee S-W, Shin J-H (2007) Implementation of a 3-dimensional game for developing balanced brainwave. In: 5th ACIS international conference on software engineering research, management & applications, 2007. SERA 2007. IEEE, pp 751–758

  • Sivanathan A et al (2014) Temporal multimodal data synchronisation for the analysis of a game driving task using EEG. Entertain Comput 5(4):323–334

    Article  Google Scholar 

  • Spüler M, Niethammer C (2015) Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity. Front Hum Neurosci 9:155

    Google Scholar 

  • Strehl U et al (2006) Self-regulation of slow cortical potentials: a new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics 118(5):e1530–e1540

    Article  Google Scholar 

  • Su L, Wenyan M, Qiqian H. The mind garden: a brain computer interface game. https://wiki.cc.gatech.edu/designcomp/images/2/24/MindGardenFinalReport.pdf

  • Taylor DM, Tillery SIH, Schwartz AB (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296(5574):1829–1832

    Article  Google Scholar 

  • Teo G et al (2015) Comparison of measures used to assess the workload of monitoring an unmanned system in a simulation mission. Proc Manuf 3:1006–1013

    Google Scholar 

  • Thomas KP, Vinod A, Guan C (2013) Design of an online EEG based neurofeedback game for enhancing attention and memory. In: 2013 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 433–436

  • Vachiratamporn V, Legaspi R, Numao M (2013) Towards the design and development of anticipation-based event selection modeling for survival horror games. In: Vachiratamporn V, Legaspi R, Numao M (eds) Theory and practice of computation. Springer, Berlin, pp 184–194

    Chapter  Google Scholar 

  • van de Laar B et al (2013) Experiencing BCI control in a popular computer game. IEEE Trans Computat Intell AI Games 5(2):176–184

    Article  Google Scholar 

  • van Erp JB, Brouwer A-M (2014) Touch-based brain computer interfaces: state of the art. In: 2014 IEEE haptics symposium (HAPTICS). IEEE, pp 397–401

  • van Vliet M et al (2012) Designing a brain–computer interface controlled video-game using consumer grade EEG hardware. In: Biosignals and biorobotics conference (BRC), 2012 ISSNIP. IEEE, pp 1–6

  • Velliste M et al (2008) Cortical control of a prosthetic arm for self-feeding. Nature 453(7198):1098–1101

    Article  Google Scholar 

  • Vidal JJ (1977) Real-time detection of brain events in EEG. Proc IEEE 65(5):633–641

    Article  Google Scholar 

  • Vidaurre C, Blankertz B (2010) Towards a cure for BCI illiteracy. Brain Topogr 23(2):194–198

    Article  Google Scholar 

  • Vourvopoulos A et al (2016a) Usability and cost-effectiveness in brain–computer interaction: Is it user throughput or technology related? In: Proceedings of the 7th augmented human international conference 2016. ACM, p 19

  • Vourvopoulos A, i Badia SB (2016) Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J Neuroeng Rehabil 13(1):69

    Article  Google Scholar 

  • Vourvopoulos A, Ferreira A, Badia SBi (2016b) NeuRow: an immersive VR environment for motor-imagery training with the use of brain–computer interfaces and vibrotactile feedback. In: Proceedings of the 3rd international conference on physiological computing systems, Lisbon, Portugal

  • Wang C et al (2007) Introduction to NeuroComm: A platform for developing real-time EEG-based brain–computer interface applications. In: 29th annual international conference of the IEEE Engineering in Medicine and Biology Society, 2007 (EMBS 2007). IEEE, pp 4703–4706

  • Wang Q, Sourina O, Nguyen MK (2010) Eeg-based “ serious” games design for medical applications. In: 2010 international conference on cyberworlds (cw). IEEE, pp 270–276

  • Washburn DA (2003) The games psychologists play (and the data they provide). Behav Res Methods 35(2):185–193

    Article  Google Scholar 

  • Wei P (2010) An virtual vehicle control game for online brain computer interface feedback training. In: 2010 international conference on mechatronics and automation (ICMA). IEEE, pp 1942–1944

  • Wolpaw JR et al (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791

    Article  Google Scholar 

  • Wong CM et al (2015) A multi-channel SSVEP-based BCI for computer games with analogue control. In: 2015 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications. IEEE, pp 1–6

  • Wu G, Xie Z, Wang X (2014) Development of a mind-controlled android racing game using a brain computer interface (BCI). In: 2014 4th IEEE international conference on information science and technology (ICIST). IEEE, pp 652–655

  • Yan S et al (2016) Enhancing audience engagement in performing arts through an adaptive virtual environment with a brain–computer interface. In: Proceedings of the 21st international conference on intelligent user interfaces. ACM, pp 306–316

  • Yoh M-S, Kwon J, Kim S (2010) Neurowander: a BCI game in the form of interactive fairy tale. In: Proceedings of the 12th ACM international conference adjunct papers on ubiquitous computing-adjunct. ACM, pp 389–390

  • Yoon H et al (2013) Emotion recognition of serious game players using a simple brain computer interface. In: 2013 international conference on ICT convergence (ICTC). IEEE, pp 783–786

  • Zander TO, Kothe C et al (2010) Enhancing human-computer interaction with input from active and passive brain-computer interfaces. In: Zander TO, Kothe C, Jatzev S, Gaertner M (eds) Brain-computer interfaces. Springer, Berlin, pp 181–199

    Chapter  Google Scholar 

  • Zander TO, Kothe C (2011) Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. J Neural Eng 8(2):025005

    Article  Google Scholar 

  • Zhao Q, Zhang L, Cichocki A (2009) EEG-based asynchronous BCI control of a car in 3D virtual reality environments. Chin Sci Bull 54(1):78–87

    Article  Google Scholar 

  • Zhu D et al (2010) A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci 2010:1

    Article  Google Scholar 

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  1. HCI Lab, Masaryk University, Botanicka 68a, Brno, Czech Republic

    Bojan Kerous, Filip Skola & Fotis Liarokapis

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  1. Bojan Kerous
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  2. Filip Skola
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Correspondence to Bojan Kerous.

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Kerous, B., Skola, F. & Liarokapis, F. EEG-based BCI and video games: a progress report. Virtual Reality 22, 119–135 (2018). https://doi.org/10.1007/s10055-017-0328-x

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