Making Food with the Mind: Integrating Brain-Computer Interface and 3D Food Fabrication

  • Nutchanon Ninyawee
  • Tawan Thintawornkul
  • Pat PataranutapornEmail author
  • Bank Ngamarunchot
  • Sirawaj Sean Itthipuripat
  • Theerawit Wilaiprasitporn
  • Kotchakan Promnara
  • Potiwat Ngamkajornwiwat
  • Werasak Surareungchai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)


We presented “Mind-Controlled 3D Printer” that translates brain signals from the user into 3D printed food. This system integrated an EEG recording device that measures neural activities in real-time with a machine learning algorithm that classify emotional valence and arousal levels, which determine the shape and size of the food fabricated by the food 3D printer. This research introduced the opportunity for combining brain-computer interface (BCI), affective computing, and additive manufacturing technology, which will ultimately enable the automation of mind to matter materialization. We demonstrated three use cases and envisioned the future research on BCI and food fabrication.


Brain-Computer Interface 3D fabrication Food printing Edible material Cognitive Food Affective computing 


  1. 1.
    Burneleit, E., Hemmert, F., Wettach, R.: Living interfaces: the impatient toaster. In: Proceedings of the 3rd International Conference on Tangible and Embedded Interaction, pp. 21–22. ACM (2009)Google Scholar
  2. 2.
    Dallman, M.F.: Stress-induced obesity and the emotional nervous system. Trends Endocrinol. Metab. 21(3), 159–165 (2010)CrossRefGoogle Scholar
  3. 3.
    Höök, K.: Affective computing (2012)Google Scholar
  4. 4.
    Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3146–3154 (2017)Google Scholar
  5. 5.
    Khot, R.A., Pennings, R., Mueller, F.: EdiPulse: supporting physical activity with chocolate printed messages. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1391–1396. ACM (2015)Google Scholar
  6. 6.
    Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)CrossRefGoogle Scholar
  7. 7.
    Kosmyna, N., Tarpin-Bernard, F., Bonnefond, N., Rivet, B.: Feasibility of BCI control in a realistic smart home environment. Front. Hum. Neurosci. 10, 416 (2016)CrossRefGoogle Scholar
  8. 8.
    Lee, B., Hong, J., Surh, J., Saakes, D.: Ori-mandu: Korean dumpling into whatever shape you want. In: Proceedings of the 2017 Conference on Designing Interactive Systems, pp. 929–941. ACM (2017)Google Scholar
  9. 9.
    Maynes-Aminzade, D.: Edible bits: seamless interfaces between people, data and food. In: Conference on Human Factors in Computing Systems (CHI 2005)-Extended Abstracts, pp. 2207–2210. Citeseer (2005)Google Scholar
  10. 10.
    Melcer, E., Isbister, K.: Motion, emotion, and form: exploring affective dimensions of shape. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1430–1437. ACM (2016)Google Scholar
  11. 11.
    Mothersill, P., Bove, Jr., V.M.: The emotivemodeler: an emotive form design cad tool. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA 2015, pp. 339–342. ACM, New York (2015).,
  12. 12.
    Murer, M., Aslan, I., Tscheligi, M.: LOLLio: exploring taste as playful modality. In: Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction, pp. 299–302. ACM (2013)Google Scholar
  13. 13.
    Musk, E., et al.: An integrated brain-machine interface platform with thousands of channels. BioRxiv p. 703801 (2019)Google Scholar
  14. 14.
    Narumi, T., Kajinami, T., Tanikawa, T., Hirose, M.: Meta cookie. In: ACM SIGGRAPH 2010 Emerging Technologies, SIGGRAPH 2010, pp. 18:1–18:1. ACM, New York (2010).,
  15. 15.
    Obrist, M.: Don’t just look – smell, taste, and feel the interaction. In: Proceedings of the 26th ACM International Conference on Multimedia, MM 2018, pp. 182. ACM, New York (2018).,
  16. 16.
    Obrist, M., Marti, P., Velasco, C., Tu, Y.T., Narumi, T., Møller, N.L.H.: The future of computing and food: extended abstract. In: Proceedings of the 2018 International Conference on Advanced Visual Interfaces, AVI 2018, pp. 5:1–5:3. ACM, New York (2018).,
  17. 17.
    Oliver, G., Wardle, J.: Perceived effects of stress on food choice. Physiol. Behav. 66(3), 511–515 (1999)CrossRefGoogle Scholar
  18. 18.
    Oliver, G., Wardle, J., Gibson, E.L.: Stress and food choice: a laboratory study. Psychosom. Med. 62(6), 853–865 (2000)CrossRefGoogle Scholar
  19. 19.
    Picard, R.W.: Affective Computing. MIT Press, Cambridge (2000)CrossRefGoogle Scholar
  20. 20.
    Shilkrot, R., Huber, J., Boldu, R., Maes, P., Nanayakkara, S.: Assistive Augmentation. Springer, Singapore (2018)Google Scholar
  21. 21.
    Sun, J., Zhou, W., Yan, L., Huang, D., Lin, L.Y.: Extrusion-based food printing for digitalized food design and nutrition control. J. Food Eng. 220, 1–11 (2018)CrossRefGoogle Scholar
  22. 22.
    Tan, C., Toh, W.Y., Wong, G., Lin, L.: Extrusion-based 3D food printing–materials and machines (2018)Google Scholar
  23. 23.
    Thayer, R.E.: The Biopsychology of Mood and Arousal. Oxford University Press, New York (1989)Google Scholar
  24. 24.
    Tripathi, S., Acharya, S., Sharma, R.D., Mittal, S., Bhattacharya, S.: Using deep and convolutional neural networks for accurate emotion classification on deap dataset. In: Twenty-Ninth IAAI Conference (2017)Google Scholar
  25. 25.
    Umematsu, T., Sano, A., Taylor, S., Picard, R.: Improving students’ daily lifestress forecasting using LSTM neural networks. In: IEEE-EMBS Biomedical and Health Informatics 2019 (2019)Google Scholar
  26. 26.
    Vi, C.T., Ablart, D., Arthur, D., Obrist, M.: Gustatory interface: the challenges of ‘how’ to stimulate the sense of taste. In: Proceedings of the 2Nd ACM SIGCHI International Workshop on Multisensory Approaches to Human-Food Interaction, MHFI 2017, pp. 29–33. , ACM, New York (2017).,
  27. 27.
    Wang, W., Yao, L., Zhang, T., Cheng, C.Y., Levine, D., Ishii, H.: Transformative appetite: shape-changing food transforms from 2D to 3D by water interaction through cooking. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 6123–6132. ACM (2017)Google Scholar
  28. 28.
    Wei, J., Ma, X., Zhao, S.: Food messaging: using edible medium for social messaging. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2873–2882. ACM (2014)Google Scholar
  29. 29.
    Zellner, D.A., Loaiza, S., Gonzalez, Z., Pita, J., Morales, J., Pecora, D., Wolf, A.: Food selection changes under stress. Physiol. Behav. 87(4), 789–793 (2006)CrossRefGoogle Scholar
  30. 30.
    Zhang, B., Wang, J., Fuhlbrigge, T.: A review of the commercial brain-computer interface technology from perspective of industrial robotics. In: 2010 IEEE International Conference on Automation and Logistics, pp. 379–384. IEEE (2010)Google Scholar
  31. 31.
    Zoran, A., Coelho, M.: Cornucopia: the concept of digital gastronomy. Leonardo 44(5), 425–431 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Nutchanon Ninyawee
    • 1
  • Tawan Thintawornkul
    • 1
  • Pat Pataranutaporn
    • 1
    • 2
    Email author
  • Bank Ngamarunchot
    • 1
  • Sirawaj Sean Itthipuripat
    • 1
  • Theerawit Wilaiprasitporn
    • 3
  • Kotchakan Promnara
    • 1
  • Potiwat Ngamkajornwiwat
    • 1
  • Werasak Surareungchai
    • 1
  1. 1.Futuristic Research Cluster (FREAK Lab)BangkokThailand
  2. 2.Massachusetts Institute of Technology (MIT)CambridgeUSA
  3. 3.Vidyasirimedhi Institute of Science and Technology (VISTEC)RayongThailand

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