Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Playing With Neuroscience: Past, Present and Future of Neuroimaging and Games (2403.15413v1)

Published 6 Mar 2024 in q-bio.NC and cs.AI

Abstract: Videogames have been a catalyst for advances in many research fields, such as artificial intelligence, human-computer interaction or virtual reality. Over the years, research in fields such as artificial intelligence has enabled the design of new types of games, while games have often served as a powerful tool for testing and simulation. Can this also happen with neuroscience? What is the current relationship between neuroscience and games research? what can we expect from the future? In this article, we'll try to answer these questions, analysing the current state-of-the-art at the crossroads between neuroscience and games and envisioning future directions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (74)
  1. G. W. Lindsay, “Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future,” Journal of Cognitive Neuroscience, vol. 33, no. 10, pp. 2017–2031, Sep. 2021, arXiv:2001.07092 [cs, q-bio].
  2. C. Hegedues, J. P. Dias Constantino, L. Dixen, and P. Burelli, “Investigating Perceived and Mechanical Challenge in Games Through Cognitive Activity,” in 2023 IEEE Conference on Games (CoG), Aug. 2023, pp. 1–4, iSSN: 2325-4289.
  3. E. P. Nuñez Castellar, J.-N. Antons, D. Marinazzo, and J. Van Looy, “Being in the zone: Using behavioral and EEG recordings for the indirect assessment of flow,” PeerJ Preprints, preprint, Sep. 2016.
  4. S. Roy, M. Islam, M. S. U. Yusuf, and N. Jahan, “EEG based stress analysis using rhythm specific spectral feature for video game play,” Computers in Biology and Medicine, vol. 148, p. 105849, Sep. 2022.
  5. F. Yamada, “Frontal midline theta rhythm and eyeblinking activity during a VDT task and a video game: useful tools for psychophysiology in ergonomics,” Ergonomics, vol. 41, no. 5, pp. 678–688, May 1998, study EEG during VDT tasks. Theta strength proportional to engagement/interest.
  6. M. A. Schier, “Changes in EEG alpha power during simulated driving: a demonstration,” International Journal of Psychophysiology, vol. 37, no. 2, pp. 155–162, Aug. 2000.
  7. D. Delisle-Rodriguez, H. L. De Oliveira, J. C. Da Silva, M. L. De Souza, T. Bastos, E. M. Nakamura-Palacios, and A. Frizera-Neto, “Multi-channel EEG-based BCI using regression and classification methods for attention training by serious game,” Biomedical Signal Processing and Control, vol. 85, p. 104937, Aug. 2023.
  8. T. Jordan and M. Dhamala, “Video game players have improved decision-making abilities and enhanced brain activities,” Neuroimage: Reports, vol. 2, no. 3, p. 100112, Sep. 2022.
  9. H. Huang and C. Cheng, “The Benefits of Video Games on Brain Cognitive Function: A Systematic Review of Functional Magnetic Resonance Imaging Studies,” Applied Sciences, vol. 12, no. 11, p. 5561, Jan. 2022, number: 11 Publisher: Multidisciplinary Digital Publishing Institute.
  10. C. D. Ferrie, P. D. Marco, R. A. Grünewald, S. Giannakodimos, and C. P. Panayiotopoulos, “Video game induced seizures.” Journal of Neurology, Neurosurgery & Psychiatry, vol. 57, no. 8, pp. 925–931, Aug. 1994, publisher: BMJ Publishing Group Ltd Section: Research Article.
  11. B. Kerous, F. Skola, and F. Liarokapis, “EEG-based BCI and video games: a progress report,” Virtual Reality, vol. 22, no. 2, pp. 119–135, Jun. 2018.
  12. G. A. M. Vasiljevic and L. C. De Miranda, “Brain–Computer Interface Games Based on Consumer-Grade EEG Devices: A Systematic Literature Review,” International Journal of Human–Computer Interaction, vol. 36, no. 2, pp. 105–142, Jan. 2020.
  13. D. Marshall, D. Coyle, S. Wilson, and M. Callaghan, “Games, Gameplay, and BCI: The State of the Art,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 5, no. 2, pp. 82–99, Jun. 2013, conference Name: IEEE Transactions on Computational Intelligence and AI in Games.
  14. D. P.-O. Bos, B. Reuderink, B. van de Laar, H. Gürkök, C. Mühl, M. Poel, D. Heylen, and A. Nijholt, “Human-Computer Interaction for BCI Games: Usability and User Experience,” in 2010 International Conference on Cyberworlds, Oct. 2010, pp. 277–281.
  15. J. Aguado-Delgado, J.-M. Gutiérrez-Martínez, J. R. Hilera, L. de Marcos, and S. Otón, “Accessibility in video games: a systematic review,” Universal Access in the Information Society, vol. 19, no. 1, pp. 169–193, Mar. 2020.
  16. G. N. Yannakakis and D. Melhart, “Affective Game Computing: A Survey,” Proceedings of the IEEE, vol. 111, no. 10, pp. 1423–1444, Oct. 2023, conference Name: Proceedings of the IEEE.
  17. O. R. Pinheiro, L. R. G. Alves, M. F. M. Romero, and J. R. de Souza, “Wheelchair simulator game for training people with severe disabilities,” in 2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW), Dec. 2016, pp. 1–8.
  18. M. Teplan, “FUNDAMENTALS OF EEG MEASUREMENT,” MEASUREMENT SCIENCE REVIEW, vol. 2, 2002.
  19. N. K. Logothetis, “What we can do and what we cannot do with fMRI,” Nature, vol. 453, no. 7197, pp. 869–878, Jun. 2008, number: 7197 Publisher: Nature Publishing Group.
  20. M.-H. J. Lin, S. N. Cross, W. J. Jones, and T. L. Childers, “Applying EEG in consumer neuroscience,” European Journal of Marketing, vol. 52, no. 1/2, pp. 66–91, Feb. 2018.
  21. A. Delorme, J. Palmer, J. Onton, R. Oostenveld, and S. Makeig, “Independent EEG Sources Are Dipolar,” PLoS ONE, vol. 7, no. 2, p. e30135, Feb. 2012.
  22. K. J. Friston, A. P. Holmes, K. J. Worsley, J.-P. Poline, C. D. Frith, and R. S. J. Frackowiak, “Statistical parametric maps in functional imaging: A general linear approach,” Human Brain Mapping, vol. 2, no. 4, pp. 189–210, 1994.
  23. Y. Harel, B. Pinsard, J. Boyle, A. Cyr, M. L. Clei, P.-H. Mignot, M. St-Laurent, K. Jerbi, and P. Bellec, “Gamer in the scanner : Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content,” Feb. 2024, publisher: OSF.
  24. N. Badinand-Hubert, M. Bureau, E. Hirsch, P. Masnou, L. Nahum, D. Parain, and R. Naquet, “Epilepsies and video games: results of a multicentric study1,” Electroencephalography and Clinical Neurophysiology, vol. 107, no. 6, pp. 422–427, Dec. 1998.
  25. H. Aliyari, M. Kazemi, E. Tekieh, M. Salehi, H. Sahraei, M. R. Daliri, H. Agaei, B. Minaei-Bidgoli, R. Lashgari, N. Srahian, M. M. Hadipour, M. Salehi, and A. Ranjbar Aghdam, “The Effects of Fifa 2015 Computer Games on Changes in Cognitive, Hormonal and Brain Waves Functions of Young Men Volunteers,” Basic and Clinical Neuroscience, vol. 6, no. 3, pp. 193–201, Jul. 2015.
  26. S. Peracchia and G. Curcio, “Exposure to video games: effects on sleep and on post-sleep cognitive abilities. A sistematic review of experimental evidences,” Sleep Science, vol. 11, no. 4, pp. 302–314, 2018.
  27. C. V. Russoniello, K. O’Brien, and J. M. Parks, “EEG, HRV and Psychological Correlates while Playing Bejeweled II: A Randomized Controlled Study,” Studies in health technology and informatics, vol. 144, pp. 189–192, 2009, casual games have a measurable impact on relaxation estimated with poms, eeg and hrv.
  28. T. Mondéjar, R. Hervás, E. Johnson, C. Gutierrez, and J. M. Latorre, “Correlation between videogame mechanics and executive functions through EEG analysis,” Journal of Biomedical Informatics, vol. 63, pp. 131–140, Oct. 2016.
  29. B. Wan, Q. Wang, K. Su, C. Dong, W. Song, and M. Pang, “Measuring the Impacts of Virtual Reality Games on Cognitive Ability Using EEG Signals and Game Performance Data,” IEEE Access, vol. 9, pp. 18 326–18 344, 2021.
  30. S.-Y. Bae, H.-Y. Cho, S.-H. Choi, and M.-H. Oh, “The Effects of Game Play Activities on the EEG, Social Skills and the Self-control of the Children with Intellectual Disabilities in ICT era,” The Journal of the Korea institute of electronic communication sciences, vol. 11, no. 8, pp. 807–816, 2016, publisher: Korea Institute of Electronic Communication Science.
  31. S. Shenjie, K. P. Thomas, K. Smitha, and A. Vinod, “Two player EEG-based neurofeedback ball game for attention enhancement,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 2014, pp. 3150–3155, iSSN: 1062-922X.
  32. K. P. Thomas, A. P. Vinod, and C. Guan, “Enhancement of attention and cognitive skills using EEG based neurofeedback game,” in 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), Nov. 2013, pp. 21–24, iSSN: 1948-3554.
  33. S. Ballesteros, J. Mayas, E. Ruiz-Marquez, A. Prieto, P. Toril, L. P. d. Leon, M. L. d. Ceballos, and J. M. R. Avilés, “Effects of Video Game Training on Behavioral and Electrophysiological Measures of Attention and Memory: Protocol for a Randomized Controlled Trial,” JMIR Research Protocols, vol. 6, no. 1, p. e6570, Jan. 2017, company: JMIR Research Protocols Distributor: JMIR Research Protocols Institution: JMIR Research Protocols Label: JMIR Research Protocols Publisher: JMIR Publications Inc., Toronto, Canada.
  34. A. E. Alchalabi, S. Shirmohammadi, A. N. Eddin, and M. Elsharnouby, “FOCUS: Detecting ADHD Patients by an EEG-Based Serious Game,” IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 7, pp. 1512–1520, Jul. 2018.
  35. P. Israsena, S. Jirayucharoensak, S. Hemrungrojn, and S. Pan-Ngum, “Brain Exercising Games With Consumer-Grade Single-Channel Electroencephalogram Neurofeedback: Pre-Post Intervention Study,” JMIR Serious Games, vol. 9, no. 2, p. e26872, Jun. 2021, company: JMIR Serious Games Distributor: JMIR Serious Games Institution: JMIR Serious Games Label: JMIR Serious Games Publisher: JMIR Publications Inc., Toronto, Canada.
  36. E. C. McWilliams, F. M. Barbey, J. F. Dyer, M. N. Islam, B. McGuinness, B. Murphy, H. Nolan, P. Passmore, L. M. Rueda-Delgado, and A. R. Buick, “Feasibility of Repeated Assessment of Cognitive Function in Older Adults Using a Wireless, Mobile, Dry-EEG Headset and Tablet-Based Games,” Frontiers in Psychiatry, vol. 12, p. 574482, Jun. 2021.
  37. G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, and R. Rupp, “EEG-based neuroprosthesis control: A step towards clinical practice,” Neuroscience Letters, vol. 382, no. 1, pp. 169–174, Jul. 2005.
  38. N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor, “A spelling device for the paralysed,” Nature, vol. 398, no. 6725, pp. 297–298, Mar. 1999, number: 6725 Publisher: Nature Publishing Group.
  39. N. Jamil, A. N. Belkacem, S. Ouhbi, and A. Lakas, “Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review,” Sensors, vol. 21, no. 14, p. 4754, Jul. 2021.
  40. M. K. Islam and A. Rastegarnia, “Editorial: Recent advances in EEG (non-invasive) based BCI applications,” Frontiers in Computational Neuroscience, vol. 17, 2023.
  41. J. Peksa and D. Mamchur, “State-of-the-Art on Brain-Computer Interface Technology,” Sensors, vol. 23, no. 13, p. 6001, Jun. 2023.
  42. K. Värbu, N. Muhammad, and Y. Muhammad, “Past, Present, and Future of EEG-Based BCI Applications,” Sensors, vol. 22, no. 9, p. 3331, Jan. 2022, number: 9 Publisher: Multidisciplinary Digital Publishing Institute.
  43. K. Sumi, K. Yabuki, T. James Tiam-Lee, A. Nasreddine Belkacem, Q. Ferre, S. Hirai, and T. Endo, “A Cooperative Game Using the P300 EEG-Based Brain-Computer Interface,” in Assistive and Rehabilitation Engineering, Y. Rybarczyk, Ed.   IntechOpen, Dec. 2019.
  44. B. Koo, H.-G. Lee, Y. Nam, and S. Choi, “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), Aug. 2015, pp. 1103–1106, iSSN: 1558-4615.
  45. F. Lotte, “Brain-Computer Interfaces for 3D Games: Hype or Hope?” in Proceedings of the 6th International Conference on Foundations of Digital Games, 2011, pp. 325–327, review on BCI.
  46. A. Ali and S. Puthusserypady, “A 3D learning playground for potential attention training in ADHD: A brain computer interface approach,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2015, pp. 67–70, 2015.
  47. D. Coyle, J. Garcia, A. R. Satti, and T. M. McGinnity, “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).   Paris, France: IEEE, Apr. 2011, pp. 1–7, method for motor imagery eeg prerpocessing to increase BCI accuracy.
  48. C. Yang, Y. Ye, X. Li, and R. Wang, “Development of a neuro-feedback game based on motor imagery EEG,” Multimedia Tools and Applications, vol. 77, no. 12, pp. 15 929–15 949, Jun. 2018.
  49. L. Kauhanen, P. Jylänki, J. Lehtonen, P. Rantanen, H. Alaranta, and M. Sams, “EEG-Based Brain-Computer Interface for Tetraplegics,” Computational Intelligence and Neuroscience, vol. 2007, pp. 1–11, 2007, bCI to move circle on screen with motor imagery. Different methods work for healthy and disabled.
  50. G. Amprimo, I. Rechichi, C. Ferraris, and G. Olmo, “Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study,” Electronics, vol. 12, no. 3, p. 623, Jan. 2023.
  51. M. Van Vliet, A. Robben, N. Chumerin, N. V. Manyakov, A. Combaz, and M. M. Van Hulle, “Designing a brain-computer interface controlled video-game using consumer grade EEG hardware,” in 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC).   Manaus, Brazil: IEEE, Jan. 2012, pp. 1–6, bCI using consumer hardware.
  52. J. Wiemeyer, L. Nacke, C. Moser, and F. Floyd Mueller, “Player Experience,” in Serious Games: Foundations, Concepts and Practice.   Cham: Springer International Publishing, 2016, pp. 243–271.
  53. C. Pedersen, J. Togelius, and G. Yannakakis, “Modeling Player Experience for Content Creation,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 2, no. 1, pp. 54–67, Mar. 2010.
  54. V. V. Abeele, K. Spiel, L. Nacke, D. Johnson, and K. Gerling, “Development and validation of the player experience inventory: A scale to measure player experiences at the level of functional and psychosocial consequences,” International Journal of Human-Computer Studies, vol. 135, p. 102370, Mar. 2020.
  55. R. M. Ryan, C. S. Rigby, and A. Przybylski, “The Motivational Pull of Video Games: A Self-Determination Theory Approach,” Motivation and Emotion, vol. 30, no. 4, pp. 344–360, Dec. 2006.
  56. D. Melhart, A. Azadvar, A. Canossa, A. Liapis, and G. N. Yannakakis, “Your Gameplay Says It All: Modelling Motivation in Tom Clancy’s The Division,” in 2019 IEEE Conference on Games (CoG), Aug. 2019, pp. 1–8, iSSN: 2325-4289.
  57. A. Drachen, “Behavioral Telemetry in Games User Research,” in Game User Experience Evaluation, ser. Human–Computer Interaction Series, R. Bernhaupt, Ed.   Cham: Springer International Publishing, 2015, pp. 135–165.
  58. P. Burelli, G. Triantafyllidis, and I. Patras, “Non-invasive player experience estimation from body motion and game context,” in 2014 IEEE Conference on Computational Intelligence and Games, Aug. 2014, pp. 1–7, iSSN: 2325-4289.
  59. G. N. Yannakakis, H. P. Martinez, and M. Garbarino, “Psychophysiology in Games,” in Emotion in Games.   Cham: Springer International Publishing, 2016, vol. 4, pp. 119–137.
  60. R. Berta, F. Bellotti, A. De Gloria, D. Pranantha, and C. Schatten, “Electroencephalogram and Physiological Signal Analysis for Assessing Flow in Games,” Computational Intelligence and AI in Games, IEEE Transactions on, vol. 5, pp. 164–175, Jun. 2013, operationalising flow with EEG.
  61. S. C. H. Kollias George Caridakis, Kostas Karpouzis, “Affective Natural Interaction Using EEG: Technologies, Applications and Future Directions,” in Semantic Multimedia Analysis and Processing.   CRC Press, 2014, num Pages: 24.
  62. T. McMahan, I. Parberry, and T. D. Parsons, “Evaluating Player Task Engagement and Arousal Using Electroencephalography,” Procedia Manufacturing, vol. 3, pp. 2303–2310, Jan. 2015.
  63. R. R. Wehbe and L. Nacke, “An Introduction to EEG Analysis Techniques and Brain-Computer Interfaces for Games User Researchers,” in DiGRA ’13 - Proceedings of the 2013 DiGRA International Conference: DeFragging Game Studies, vol. 7, 2014.
  64. A. Stein, Y. Yotam, R. Puzis, G. Shani, and M. Taieb-Maimon, “EEG-triggered dynamic difficulty adjustment for multiplayer games,” Entertainment Computing, vol. 25, pp. 14–25, Mar. 2018.
  65. T. B. Alakus, M. Gonen, and I. Turkoglu, “Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO,” Biomedical Signal Processing and Control, vol. 60, p. 101951, Jul. 2020.
  66. M. Duvinage, T. Castermans, M. Petieau, T. Hoellinger, G. Cheron, and T. Dutoit, “Performance of the Emotiv Epoc headset for P300-based applications,” BioMedical Engineering OnLine, vol. 12, no. 1, p. 56, Jun. 2013.
  67. D. Melhart, A. Liapis, and G. N. Yannakakis, “The Arousal Video Game AnnotatIoN (AGAIN) Dataset,” IEEE Transactions on Affective Computing, vol. 13, no. 4, pp. 2171–2184, Oct. 2022, conference Name: IEEE Transactions on Affective Computing.
  68. T. Grootswagers, I. Zhou, A. K. Robinson, M. N. Hebart, and T. A. Carlson, “Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams,” Scientific Data, vol. 9, no. 1, p. 3, Jan. 2022, number: 1 Publisher: Nature Publishing Group.
  69. V. Jayaram and A. Barachant, “MOABB: trustworthy algorithm benchmarking for BCIs,” Journal of Neural Engineering, vol. 15, no. 6, p. 066011, Sep. 2018, publisher: IOP Publishing.
  70. K. J. Gorgolewski, T. Auer, V. D. Calhoun, R. C. Craddock, S. Das, E. P. Duff, G. Flandin, S. S. Ghosh, T. Glatard, Y. O. Halchenko, D. A. Handwerker, M. Hanke, D. Keator, X. Li, Z. Michael, C. Maumet, B. N. Nichols, T. E. Nichols, J. Pellman, J.-B. Poline, A. Rokem, G. Schaefer, V. Sochat, W. Triplett, J. A. Turner, G. Varoquaux, and R. A. Poldrack, “The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments,” Scientific Data, vol. 3, no. 1, p. 160044, Jun. 2016, publisher: Nature Publishing Group.
  71. C. Westlin, J. E. Theriault, Y. Katsumi, A. Nieto-Castanon, A. Kucyi, S. F. Ruf, S. M. Brown, M. Pavel, D. Erdogmus, D. H. Brooks, K. S. Quigley, S. Whitfield-Gabrieli, and L. F. Barrett, “Improving the study of brain-behavior relationships by revisiting basic assumptions,” Trends in Cognitive Sciences, vol. 27, no. 3, pp. 246–257, Mar. 2023.
  72. A. H. Marblestone, G. Wayne, and K. P. Kording, “Toward an Integration of Deep Learning and Neuroscience,” Frontiers in Computational Neuroscience, vol. 10, 2016.
  73. N. Kriegeskorte, “Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing,” Annual Review of Vision Science, vol. 1, no. 1, pp. 417–446, 2015, _eprint: https://doi.org/10.1146/annurev-vision-082114-035447.
  74. D. Bethge, P. Hallgarten, T. Grosse-Puppendahl, M. Kari, R. Mikut, A. Schmidt, and O. Özdenizci, “Domain-Invariant Representation Learning from EEG with Private Encoders,” May 2022, arXiv:2201.11613 [cs].

Summary

We haven't generated a summary for this paper yet.