The CoDIS Taxonomy for Brain-Computer Interface Games Controlled by Electroencephalography

References

• Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1), 011001. https://doi.org/10.1088/1741-2552/aaf12e
   PubMed Web of Science ®Google Scholar
• Adams, E. (2014). Fundamentals of game design (3rd ed.). New Riders Publishing.
   Google Scholar
• Ahn, S., Kim, K., & Jun, S. C. (2016). Steady-state somatosensory evoked potential for brain-computer interface–present and future. Frontiers in Human Neuroscience, 9, 716. https://doi.org/10.3389/fnhum.2015.00716
   PubMed Web of Science ®Google Scholar
• Akhtar, A., Norton, J. J. S., Kasraie, M., & Bretl, T. (2014). Playing checkers with your mind: An interactive multiplayer hardware game platform for brain-computer interfaces. In 2014 36th annual international conference of the IEEE engineering in medicine and biology society (Vol. 2014, pp. 1650–1653). IEEE. https://doi.org/10.1109/EMBC.2014.6943922
   Google Scholar
• Al-Qaysi, Z., Zaidan, B., Zaidan, A., & Suzani, M. (2018). A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations. Computer Methods and Programs in Biomedicine, 164, 221–237. https://doi.org/10.1016/j.cmpb.2018.06.012
   PubMed Web of Science ®Google Scholar
• Al-Qaysi, Z. T., Ahmed, M. A., Hammash, N. M., Hussein, A. F., Albahri, A. S., Suzani, M. S., Al-Bander, B., Shuwandy, M. L., & Salih, M. M. (2021). Systematic review of training environments with motor imagery brain–computer interface: Coherent taxonomy, open issues and recommendation pathway solution. Health and Technology, 11(4), 783–801. https://doi.org/10.1007/s12553-021-00560-8
   Web of Science ®Google Scholar
• Annese, V. F., Mezzina, G., & De Venuto, D. (2016) Towards mobile health care: Neurocognitive impairment monitoring by BCI-based game. In 2016 IEEE sensors (pp. 1–3). IEEE. https://doi.org/10.1109/ICSENS.2016.7808745
   Google Scholar
• Antle, A. N., Chesick, L., & Mclaren, E.-S. (2017) Opening up the design space of neurofeedback brain–computer interfaces for children. ACM Transactions on Computer-Human Interaction, 24(6), 1–33. https://doi.org/10.1145/3131607
   Web of Science ®Google Scholar
• Arns, M., Conners, C. K., & Kraemer, H. C. (2013) A decade of EEG theta/beta ratio research in ADHD. Journal of Attention Disorders, 17(5), 374–383. https://doi.org/10.1177/1087054712460087
   PubMed Web of Science ®Google Scholar
• Arslan, R. B., & Filiz, E. (2022). Enhancement of player experience in video games using EEG based dynamic difficulty adjustment [Paper presentation]. 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE. https://doi.org/10.1109/inista55318.2022.9894125
   Google Scholar
• Azman, N. H., Mansor, W., & Lee, K. Y. (2017). Neuro based racing car for cognitive training [Paper presentation]. 2017 IEEE 15th Student Conference on Research and Development (Scored) (pp. 473–476). IEEE. https://doi.org/10.1109/SCORED.2017.8305437
   Google Scholar
• Bablani, A., Edla, D. R., Tripathi, D., & Cheruku, R. (2020) Survey on brain-computer interface: An emerging computational intelligence paradigm. ACM Computing Surveys, 52(1), 1–32. https://doi.org/10.1145/3297713
   Web of Science ®Google Scholar
• Banville, H., & Falk, T. (2016) Recent advances and open challenges in hybrid brain-computer interfacing: A technological review of non-invasive human research. Brain-Computer Interfaces, 3(1), 9–46. https://doi.org/10.1080/2326263X.2015.1134958
   Web of Science ®Google Scholar
• Beveridge, R., Marshall, D., Wilson, S., & Coyle, D. (2015). Classification effects on Motion-Onset Visual Evoked Potentials using commercially available video games [Paper presentation]. Proceedings of the 20th International Conference on Computer Games: Ai, Animation, Mobile, Interactive Multimedia, Educational and Serious Games (pp. 28–37). https://doi.org/10.1109/CGames.2015.7272958
   Google Scholar
• Beveridge, R., Wilson, S., Callaghan, M., & Coyle, D. (2019) Neurogaming with motion-onset visual evoked potentials (MVEPs): Adults versus teenagers. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 572–581. https://doi.org/10.1109/TNSRE.2019.2904260
   PubMed Web of Science ®Google Scholar
• Bordoloi, S., Sharmah, U., & Hazarika, S. M. (2012). Motor imagery based BCI for a maze game [Paper presentation]. 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI) (pp. 1–6). IEEE. https://doi.org/10.1109/IHCI.2012.6481848
   Google Scholar
• Bos, D. P., Reuderink, B., Laar, B., Gürkök, H., Mühl, C., Poel, M., Nijholt, A., & Heylen, D. (2010). Brain-computer interfacing and games. In D. Tan & A. Nijholt (Eds.), Brain-computer interfaces (pp. 149–178). Springer.
   Google Scholar
• Choi, I., Rhiu, I., Lee, Y., Yun, M. H., & Nam, C. S. (2017). A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives. Plos One, 12(4), e0176674. https://doi.org/10.1371/journal.pone.0176674
   PubMed Web of Science ®Google Scholar
• Chumerin, N., Manyakov, N. V., van Vliet, M., Robben, A., Combaz, A., & Hulle, M. M. V. (2013). Steady-state visual evoked potential-based computer gaming on a consumer-grade EEG device. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 100–110. https://doi.org/10.1109/TCIAIG.2012.2225623
   Web of Science ®Google Scholar
• Congedo, M., Goyat, M., Tarrin, N., Ionescu, G., Varnet, L., Rivet, B., Phlypo, R., Jrad, N., Acquadro, M., & Jutten, C. (2011). “Brain invaders”: A prototype of an open-source p300-based video game working with the OpenVIBE platform [Paper presentation]. Proceedings of the 5th International Brain-Computer Interface Conference (pp. 280–283).
   Google Scholar
• Crawford, C. (1984). The art of computer game design (1st ed.). McGraw-Hill/Osborne Media.
   Google Scholar
• Cruz, I., Moreira, C., Poel, M., Ferreira, H., & Nijholt, A. (2018). Kessel run–A cooperative multiplayer SSVEP BCI game. In Lecture notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 215, pp.77–95). Springer. https://doi.org/10.1007/978-3-319-73062-2_6
   Google Scholar
• De Venuto, D., Annese, V. F., Mezzina, G., Ruta, M., & Di Sciascio, E. (2016). Brain-computer interface using P300: A gaming approach for neurocognitive impairment diagnosis [Paper presentation]. 2016 IEEE International High Level Design Validation and Test Workshop (HLDVT) (pp. 93–99). IEEE. https://doi.org/10.1109/HLDVT.2016.7748261
   Google Scholar
• Demazure, T., Karran, A. J., Boasen, J., Léger, P.-M., & Sénécal, S. (2021). Distributed remote EEG data collection for NeuroIS research: A methodological framework. In Augmented cognition (pp. 3–22). Springer International Publishing. https://doi.org/10.1007/978-3-030-78114-9_1
   Google Scholar
• de Miranda, L. C., Hornung, H. H., & Baranauskas, M. C. C. (2010). Adjustable interactive rings for iDTV. IEEE Transactions on Consumer Electronics, 56(3), 1988–1996. https://doi.org/10.1109/TCE.2010.5606356
   Web of Science ®Google Scholar
• de Miranda, L. C., Hornung, H. H., Pereira, R., & Baranauskas, M. C. C. (2013). Exploring adjustable interactive rings in game playing: Preliminary results. In Proceedings of the 2nd international conference on design, user experience and usability (DUXU)/15th international conference on human-computer interaction (HCII), Las Vegas, USA (lecture notes in computer science) (Vol. 8013, pp. 518–527). Springer.
   Google Scholar
• Fernandez, H., Mikami, K., & Kondo, K. (2016). Adaptable game experience through procedural content generation and brain computer interface [Paper presentation]. ACM SIGGRAPH 2016 Posters (SIGGRAPH’16) (p. 2). https://doi.org/10.1145/2945078.2945124
   Google Scholar
• Fernandez, H. D., Mikami, K., & Kondo, K. (2017). Adaptable game experience based on player’s performance and EEG [Paper presentation]. 2017 Nicograph International (NicoInt) (pp. 1–8). IEEE. https://doi.org/10.1109/NICOInt.2017.11
   Google Scholar
• Ferreira, A. L. S., de Miranda, L. C., de Miranda, E. E. C., & Sakamoto, S. G. (2013). A survey of interactive systems based on brain-computer interfaces. Journal on Interactive Systems, 4(1), 1. https://doi.org/10.5753/jis.2013.623
   Google Scholar
• Ferreira, A. L. S., Marciano, J. N., de Miranda, L. C., & de Miranda, E. E. C. (2014). Understanding and proposing a design rationale of digital games based on brain-computer interface: Results of the Admiralmind Battleship study. Journal on Interactive Systems, 5(1), 1. https://doi.org/10.5753/jis.2014.638
   Google Scholar
• Fiałek, S., & Liarokapis, F. (2016). Comparing two commercial brain computer interfaces for serious games and virtual environments. In Socio-affective computing (pp. 103–117). Springer. https://doi.org/10.1007/978-3-319-41316-7_6
   Google Scholar
• Gevensleben, H., Albrecht, B., Lütcke, H., Auer, T., Dewiputri, W. I., Schweizer, R., Moll, G., Heinrich, H., & Rothenberger, A. (2014). Neurofeedback of slow cortical potentials: Neural mechanisms and feasibility of a placebo-controlled design in healthy adults. Frontiers in Human Neuroscience, 8, 990. https://doi.org/10.3389/fnhum.2014.00990
   PubMed Web of Science ®Google Scholar
• Gürkök, H., Nijholt, A., Poel, M., & Obbink, M. (2013). Evaluating a multi-player brain-computer interface game: Challenge versus co-experience. Entertainment Computing, 4(3), 195–203. https://doi.org/10.1016/j.entcom.2012.11.001
   Google Scholar
• Henrik, C., Olle, H., Craig, L., Charlotte, S., Jeanette, E. (2011). The aiming game: Using a game with biofeedback for training in emotion regulation [Paper presentation]. Proceedings of the 2011 Digra International Conference: Think Design Play (Digra’11) (p. 18).
   Google Scholar
• Herz, J. C. (1997). Joystick nation: How videogames ate quarters, won our hearts, and rewired our minds (1st ed.). Little Brown and Company.
   Google Scholar
• Hjorungdal, R.-M., Sanfilippo, F., Osen, O. L., Rutle, A., & Bye, R. T. (2016). A game-based learning framework for controlling brain-actuated wheelchairs [Paper presentation]. Proceedings of the 30th European Conference on Modelling and Simulation (ECMS’16) (p. 10). https://doi.org/10.7148/2016-0554
   Google Scholar
• Jeunet, C., Debener, S., Lotte, F., Mattout, J., Scherer, R., & Zich, C. (2018). Mind the traps: Design guidelines for rigorous BCI experiments. In C. S. Nam, A. Nijholt, & F. Lotte (Eds.), Brain–computer interfaces handbook (1st ed., pp. 1–22). Taylor & Francis.
   Google Scholar
• Jiang, X., Bian, G.-B., & Tian, Z. (2019). Removal of artifacts from EEG signals: A review. Sensors, 19(5), 987. https://doi.org/10.3390/s19050987
   PubMed Web of Science ®Google Scholar
• Li, J., Liu, Y., Lu, Z., & Zhang, L. (2013). A competitive brain computer interface: Multi-person car racing system [Paper presentation]. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2200–2203). IEEE. https://doi.org/10.1109/EMBC.2013.6609972
   Google Scholar
• 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. https://doi.org/10.1016/j.neuroimage.2006.09.024
   PubMed Web of Science ®Google Scholar
• Kaplan, A. Y., Shishkin, S. L., Ganin, I. P., Basyul, I. A., & Zhigalov, A. Y. (2013). Adapting the p300-based brain–computer interface for gaming: A review. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 141–149. https://doi.org/10.1109/TCIAIG.2012.2237517
   Web of Science ®Google Scholar
• Käthner, I., Ruf, C. A., Pasqualotto, E., Braun, C., Birbaumer, N., & Halder, S. (2013). A portable auditory P300 brain-computer interface with directional cues. Clinical Neurophysiology, 124(2), 327–338. https://doi.org/10.1016/j.clinph.2012.08.006
   PubMed Web of Science ®Google Scholar
• Keller, A. S., Leikauf, J. E., Holt-Gosselin, B., Staveland, B. R., & Williams, L. M. (2019). Paying attention to attention in depression. Translational Psychiatry, 9(1), 1–12. https://doi.org/10.1038/s41398-019-0616-1
   Google Scholar
• Kerous, B., Skola, F., & Liarokapis, F. (2018). EEG-based BCI and video games: A progress report. Virtual Reality, 22(2), 119–135. https://doi.org/10.1007/s10055-017-0328-x
   Web of Science ®Google Scholar
• Koo, B., Lee, H.-G., Nam, Y., & Choi, S. (2015). Immersive BCI with SSVEP in VR head-mounted display [Paper presentation]. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. https://doi.org/10.1109/embc.2015.7318558
   Google Scholar
• Kosmyna, N., & Lécuyer, A. (2019). A conceptual space for EEG-based brain-computer interfaces. Plos One, 14(1), e0210145. https://doi.org/10.1371/journal.pone.0210145
   PubMed Web of Science ®Google Scholar
• 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 Transactions on Computational Intelligence and AI in Games, 5(2), 150–154. https://doi.org/10.1109/TCIAIG.2012.2230003
   Web of Science ®Google Scholar
• Krippendorff, K. (2019). Content analysis: An introduction to its methodology. SAGE Publications, Inc. https://doi.org/10.4135/9781071878781
   Google Scholar
• Kuba, M., Kubová, Z., Kremláček, J., & Langrová, J. (2007). Motion-onset VEPs: Characteristics, methods, and diagnostic use. Vision Research, 47(2), 189–202. https://doi.org/10.1016/j.visres.2006.09.020
   PubMed Web of Science ®Google Scholar
• Kwasnik, B. (1999). The role of classification in knowledge representation and discovery. Library Trends, 48(1), 22–47.
   Web of Science ®Google Scholar
• Laamarti, F., Eid, M., & Saddik, A. E. (2014). An overview of serious games. International Journal of Computer Games Technology, 2014(1), 1–15. https://doi.org/10.1155/2014/358152
   Google Scholar
• Ladouce, S., Darmet, L., Tresols, J. J. T., Velut, S., Ferraro, G., & Dehais, F. (2022). Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design. Scientific Reports, 12(1), 1–12. https://doi.org/10.1038/s41598-022-12733-0
   Google Scholar
• Lalor, E. C., Kelly, S. P., Finucane, C., Burke, R., Smith, R., Reilly, R. B., & McDarby, G. (2005). Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP Journal on Advances in Signal Processing, 2005(19), 3156–3164. https://doi.org/10.1155/ASP.2005.3156
   Google Scholar
• Landau, O., Puzis, R., & Nissim, N. (2021). Mind your mind: EEG-based brain-computer interfaces and their security in cyber space. ACM Computing Surveys, 53(1), 1–38. https://doi.org/10.1145/3372043
   Web of Science ®Google Scholar
• Lebedev, M. A., & Nicolelis, M. A. L. (2017). Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation. Physiological Reviews, 97(2), 767–837. https://doi.org/10.1152/physrev.00027.2016
   PubMed Web of Science ®Google Scholar
• Leite, H. M. D. A., Carvalho, S. N. D., Costa, T. B. D. S., Attux, R., Hornung, H. H., & Arantes, D. S. (2018). Analysis of user interaction with a brain-computer interface based on steady-state visually evoked potentials: Case study of a game. Computational Intelligence and Neuroscience, 2018, 1–10. https://doi.org/10.1155/2018/4920132
   Web of Science ®Google Scholar
• Li, M., Li, F., Pan, J., Zhang, D., Zhao, S., Li, J., & Wang, F. (2021). The MindGomoku: An online p300 BCI game based on Bayesian deep learning. Sensors, 21(5), 1613. https://doi.org/10.3390/s21051613
   PubMed Web of Science ®Google Scholar
• Liu, Y., Hou, X., Sourina, O., & Bazanova, O. (2016). Individual theta/beta based algorithm for neurofeedback games to improve cognitive abilities. In M. L. Gavrilova & C. K. Tan (Eds.), Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). (Vol. 9570, pp. 57–73). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-49247-5_4
   Google Scholar
• Maby, E., Perrin, M., Bertrand, O., Sanchez, G., & Mattout, J. (2012). BCI could make old two-player games even more fun: A proof of concept with “connect four. Advances in Human-Computer Interaction, 2012, 1–8. https://doi.org/10.1155/2012/124728
   Google Scholar
• Marshall, D., Coyle, D., Wilson, S., & Callaghan, M. (2013). Games, gameplay, and BCI: The state of the art. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 82–99. https://doi.org/10.1109/TCIAIG.2013.2263555
   Web of Science ®Google Scholar
• Martisius, I., & Damasevicius, R. (2016). A prototype SSVEP based real time BCI gaming system. Computational Intelligence and Neuroscience, 2016(1), 1–15. https://doi.org/10.1155/2016/3861425
   Google Scholar
• Mason, S. G., & Birch, G. E. (2003). A general framework for brain-computer interface design. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(1), 70–85. https://doi.org/10.1109/TNSRE.2003.810426
   PubMed Web of Science ®Google Scholar
• Mason, S. G., Jackson, M. M. M., & Birch, G. E. (2005). A general framework for characterizing studies of brain interface technology. Annals of Biomedical Engineering, 33(11), 1653–1670. https://doi.org/10.1007/s10439-005-7706-3
   PubMed Web of Science ®Google Scholar
• Mun, S., Park, M.-C., Park, S., & Whang, M. (2012). SSVEP and ERP measurement of cognitive fatigue caused by stereoscopic 3D. Neuroscience Letters, 525(2), 89–94. https://doi.org/10.1016/j.neulet.2012.07.049
   PubMed Web of Science ®Google Scholar
• Muñoz, J. E., Gonçalves, A., Vieira, T., Cró, D., Chisik, Y., & Bermúdez I Badia, S. (2016). Space connection–A multiplayer collaborative biofeedback game to promote empathy in teenagers: A feasibility study [Paper presentation]. Proceedings of the 3rd International Conference on Physiological Computing Systems (pp. 88–97). SCITEPRESS–Science and Technology Publications. https://doi.org/10.5220/0005948400880097
   Google Scholar
• Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain-computer interfaces, a review. Sensors, 12(2), 1211–1279. https://doi.org/10.3390/s120201211
   PubMed Web of Science ®Google Scholar
• Nijholt, A., & Gürkök, H. (2013). Multi-brain games: Cooperation and competition. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 8009 LNCS, pp. 652–661). https://doi.org/10.1007/978-3-642-39188-0_70
   Google Scholar
• Obbink, M., Gürkök, H., Bos, D. P.-O., Hakvoort, G., Poel, M., & Nijholt, A. (2012). Social interaction in a cooperative brain-computer interface game. In Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (pp. 183–192). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-30214-5_20
   Google Scholar
• Ortner, R., Lugo, Z., Noirhomme, Q., Laureys, S., & Guger, C. (2014). A tactile brain-computer interface for severely disabled patients [Paper presentation]. Proceedings of the 2014 IEEE Haptics Symposium (Haptics) (pp. 235–237). IEEE. https://doi.org/10.1109/HAPTICS.2014.6775460
   Google Scholar
• Padfield, N., Camilleri, K., Camilleri, T., Fabri, S., & Bugeja, M. (2022). A comprehensive review of endogenous EEG-based BCIs for dynamic device control. Sensors, 22(15), 5802. https://doi.org/10.3390/s22155802
   PubMed Web of Science ®Google Scholar
• Pfurtscheller, G., Allison, B. Z., Brunner, C., Bauernfeind, G., Solis-Escalante, T., Scherer, R., Zander, T. O., Mueller-Putz, G., Neuper, C., & Birbaumer, N. (2010). The hybrid BCI. Frontiers in Neuroscience, 4, 30. https://doi.org/10.3389/fnpro.2010.00003
   PubMed Web of Science ®Google Scholar
• Prieto-Díaz, R. (1991). Implementing faceted classification for software reuse. Communications of the ACM, 34(5), 88–97. https://doi.org/10.1145/103167.103176
   Web of Science ®Google Scholar
• Prpa, M., & Pasquier, P. (2019). Brain-computer interfaces in contemporary art: A state of the art and taxonomy. In Brain art (pp. 65–115). Springer International Publishing. https://doi.org/10.1007/978-3-030-14323-7_3
   Google Scholar
• Punsawad, Y., & Wongsawat, Y. (2017). Multi-command SSAEP-based BCI system with training sessions for SSVEP during an eye fatigue state. IEEJ Transactions on Electrical and Electronic Engineering, 12(S1), S72–S78. https://doi.org/10.1002/tee.22441
   Web of Science ®Google Scholar
• Putman, P., Verkuil, B., Arias-Garcia, E., Pantazi, I., & van Schie, C. (2014). EEG theta/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention. Cognitive, Affective & Behavioral Neuroscience, 14(2), 782–791. https://doi.org/10.3758/s13415-013-0238-7
   PubMed Web of Science ®Google Scholar
• Putri, F., Luca, I. S., Pedro, J. A. G., Ding, H., & Vučković, A. (2022). Winners and losers in brain computer interface competitive gaming: Directional connectivity analysis. Journal of Neural Engineering, 19(4), 046037. https://doi.org/10.1088/1741-2552/ac8451
   Web of Science ®Google Scholar
• Ramadan, R. A., & Vasilakos, A. V. (2017). Brain computer interface: Control signals review. Neurocomputing, 223, 26–44. https://doi.org/10.1016/j.neucom.2016.10.024
   Web of Science ®Google Scholar
• Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., & Volosyak, I. (2018). Brain–computer interface spellers: A review. Brain Sciences, 8(4), 57. https://doi.org/10.3390/brainsci8040057
   PubMed Web of Science ®Google Scholar
• Son, D., Rover, M., Blasio, F. M. D., Does, W., Barry, R. J., & Putman, P. (2019). Electroencephalography theta/beta ratio covaries with mind wandering and functional connectivity in the executive control network. Annals of the New York Academy of Sciences, 1452(1), 52–64. https://doi.org/10.1111/nyas.14180
   PubMed Web of Science ®Google Scholar
• Stegman, P., Crawford, C. S., Andujar, M., Nijholt, A., & Gilbert, J. E. (2020). Brain–computer interface software: A review and discussion. IEEE Transactions on Human-Machine Systems, 50(2), 101–115. https://doi.org/10.1109/THMS.2020.2968411
   Web of Science ®Google Scholar
• Stein, A., Yotam, Y., Puzis, R., Shani, G., & Taieb-Maimon, M. (2018). EEG-triggered dynamic difficulty adjustment for multiplayer games. Entertainment Computing, 25(December 2017), 14–25. https://doi.org/10.1016/j.entcom.2017.11.003
   Google Scholar
• Strehl, U., Leins, U., Goth, G., Klinger, C., Hinterberger, T., & Birbaumer, N. (2006). Self-regulation of slow cortical potentials: A new treatment for children with attention-deficit/hyperactivity disorder. Pediatrics, 118(5), e1530–e1540. https://doi.org/10.1542/peds.2005-2478
   PubMed Web of Science ®Google Scholar
• Taherian, S., Selitskiy, D., Pau, J., & Claire Davies, T. (2017). Are we there yet? Evaluating commercial grade brain–computer interface for control of computer applications by individuals with cerebral palsy. Disability and Rehabilitation. Assistive Technology, 12(2), 165–174. https://doi.org/10.3109/17483107.2015.1111943
   PubMed Web of Science ®Google Scholar
• Tzitzikas, Y., Analyti, A., Spyratos, N., & Constantopoulos, P. (2007). An algebra for specifying valid compound terms in faceted taxonomies. Data & Knowledge Engineering, 62(1), 1–40. https://doi.org/10.1016/j.datak.2006.07.005
   Web of Science ®Google Scholar
• Usman, M., Britto, R., Börstler, J., & Mendes, E. (2017). Taxonomies in software engineering: A systematic mapping study and a revised taxonomy development method. Information and Software Technology, 85, 43–59. https://doi.org/10.1016/j.infsof.2017.01.006
   Web of Science ®Google Scholar
• van de Laar, B., Gürkök, H., Bos, D. P.-O., Poel, M., & Nijholt, A. (2013). Experiencing BCI control in a popular computer game. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 176–184. https://doi.org/10.1109/TCIAIG.2013.2253778
   Web of Science ®Google Scholar
• van Erp, J., Lotte, F., & Tangermann, M. (2012). Brain-computer interfaces: Beyond medical applications. Computer Magazine. 45(4), 26–34. https://doi.org/10.1109/MC.2012.107
   Web of Science ®Google Scholar
• van Vliet, M., Robben, A., Chumerin, N., Manyakov, N. V., Combaz, A., Hulle, M. M. V. (2012). Designing a brain-computer interface controlled video-game using consumer grade EEG hardware [Paper presentation]. Proceedings of the ISSNIP Biosignals and Biorobotics Conference (BRC’12) (pp. 1–6).
   Google Scholar
• Vasiljevic, G. A. M., & de Miranda, L. C. (2020a). Brain–computer interface games based on consumer-grade EEG devices: A systematic literature review. International Journal of Human–Computer Interaction, 36(2), 105–142. https://doi.org/10.1080/10447318.2019.1612213
   Web of Science ®Google Scholar
• Vasiljevic, G. A. M., & de Miranda, L. C. (2020b). A general model for electroencephalography-controlled brain-computer interface games. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 12250 LNCS, pp. 174–189). https://doi.org/10.1007/978-3-030-58802-1_13
   Google Scholar
• Vasiljevic, G. A. M., & de Miranda, L. C. (2022). Comparing users’ performance and game experience between a competitive and collaborative brain-computer interface. Behaviour & Information Technology. Advance online publication. https://doi.org/10.1080/0144929X.2022.2152727
   Google Scholar
• Vasiljevic, G. A. M., de Miranda, L. C., & de Menezes, B. C. (2018). Mental war: An attention-based single/multiplayer brain-computer interface game. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics).(Vol. 10960 LNCS, pp. 450–465). https://doi.org/10.1007/978-3-319-95162-1_31
   Google Scholar
• Vasiljevic, G. A. M., de Miranda, L. C., & de Miranda, E. E. C. (2016). A case study of MasterMind Chess: Comparing mouse/keyboard interaction with Kinect-based gestural interface. Advances in Human-Computer Interaction, 2016(1), 1–10. https://doi.org/10.1155/2016/4602471
   Google Scholar
• Wang, Y., Nakanishi, M., & Zhang, D. (2019). EEG-based brain-computer interfaces. In Advances in experimental medicine and biology (pp. 41–65). Springer Singapore. https://doi.org/10.1007/978-981-13-2050-7_2
   Google Scholar
• Wang, Z., Yu, Y., Xu, M., Liu, Y., Yin, E., & Zhou, Z. (2019). Towards a hybrid BCI gaming paradigm based on motor imagery and SSVEP. International Journal of Human–Computer Interaction, 35(3), 197–205. https://doi.org/10.1080/10447318.2018.1445068
   Web of Science ®Google Scholar
• Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. (2012). Experimentation in software engineering (1st ed.). Springer.
   Google Scholar
• Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791. https://doi.org/10.1016/s1388-2457(02)00057-3
   PubMed Web of Science ®Google Scholar
• Wong, C. M., Tang, Q., Cruz, J. N., Wan, F. (2015). A multi-channel SSVEP-based BCI for computer games with analogue control [Paper presentation]. Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA’15) (pp. 1–6).
   Google Scholar
• Woźniak, M. P., Sikorski, P., Wróbel-Lachowska, M., Bartłomiejczyk, N., Dominiak, J., Grudzień, K., & Romanowski, A. (2021). Enhancing in-game immersion using BCI-controlled mechanics [Paper presentation]. Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology. ACM. https://doi.org/10.1145/3489849.3489862
   Google Scholar
• Wu, C.-M., Chen, Y.-J., Zaeni, I. A. E., & Chen, S.-C. (2016). A new SSVEP based BCI application on the mobile robot in a maze game [Paper presentation]. 2016 International Conference on Advanced Materials for Science and Engineering (ICAMSE) (pp. 550–553). IEEE. https://doi.org/10.1109/ICAMSE.2016.7840197
   Google Scholar
• Yin, R. K. (2009). Case study research design and methods (4th ed.). Sage Publications.
   Google Scholar
• Zander, T. O., & Kothe, C. (2011). Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general. Journal of Neural Engineering, 8(2), 025005. https://doi.org/10.1088/1741-2560/8/2/025005
   PubMed Web of Science ®Google Scholar
• Zander, T. O., Kothe, C., Jatzev, S., & Gaertner, M. (2010). Enhancing human-computer interaction with input from active and passive brain-computer interfaces. In Brain-computer interfaces (pp.181–199). https://doi.org/10.1007/978-1-84996-272-8_11
   Google Scholar
• Zander, T. O., Kothe, C., Welke, S., & Roetting, M. (2009). Utilizing secondary input from passive brain-computer interfaces for enhancing human-machine interaction. In Foundations of augmented cognition. neuroergonomics and operational neuroscience (Vol. 5638 LNCS). https://doi.org/10.1007/978-3-642-02812-0_86
   Google Scholar
• Zhang, H., Xie, J., Xiao, Y., Cui, G., Xu, G., Tao, Q., Gebrekidan, Y. Y., Yang, Y., Ren, Z., & Li, M. (2023). Steady-state auditory motion based potentials evoked by intermittent periodic virtual sound source and the effect of auditory noise on EEG enhancement. Hearing Research, 428, 108670. https://doi.org/10.1016/j.heares.2022.108670
   PubMed Web of Science ®Google Scholar
• Zhao, Q., Zhang, L., & Cichocki, A. (2009). EEG-based asynchronous BCI control of a car in 3d virtual reality environments. Chinese Science Bulletin, 54(1), 78–87. https://doi.org/10.1007/s11434-008-0547-3
   Google Scholar
• Ziebell, P., Stümpfig, J., Eidel, M., Kleih, S. C., Kübler, A., Latoschik, M. E., & Halder, S. (2020). Stimulus modality influences session-to-session transfer of training effects in auditory and tactile streaming-based p300 brain–computer interfaces. Scientific Reports, 10(1), 11873. https://doi.org/10.1038/s41598-020-67887-6
   PubMed Web of Science ®Google Scholar