Skip to main content

Mind-Media System: A Consumer-Grade Brain-Computer Interface System for Media Applications

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 268))

  • 540 Accesses

Abstract

Brain science has made many achievements in the media area. However, the brain-computer interface (BCI) is not yet available to the general public because of the cost of the equipment. This paper investigates a consumer-grade brain-computer interface system for media applications, which only cost $45. By the electroencephalogram (EEG) processing method studied in this paper, the EEG signals corresponding to different commands can be identified so that subjects could control the media by their mind without any body movements. The validation experiment on music players demonstrates the effectiveness of our mind-media system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Scholler, S., Bosse, S., Treder, M.S., Blankertz, B., Curio, G., Muller, K., Wiegand, T.: Toward a direct measure of video quality perception using EEG. IEEE Trans. Image Process. 21, 2619 (2012)

    Google Scholar 

  2. Blankertz, B., Acqualagna, L., Dahne, S., Haufe, S., Schultzekraft, M., Sturm, I., Uscumlic, M., Wenzel, M., Curio, G., Muller, K.: The Berlin brain-computer interface: progress beyond communication and control. Front. Neurosci. 10, 530 (2016)

    Google Scholar 

  3. Kamitani, Y., Tong, F.: Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679 (2005)

    Google Scholar 

  4. Rashkov, G., Bobe, A., Fastovets, D., Komarova, M.: Natural image reconstruction from brain waves: a novel visual BCI system with native feedback. bioRxiv 787101 (2019)

    Google Scholar 

  5. Ponce, C.R., Xiao, W., Schade, P.F., Hartmann, T.S., Kreiman, G., Livingstone, M.S.: Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell 177 (2019)

    Google Scholar 

  6. Shen, G., Horikawa, T., Majima, K., Kamitani, Y.: Deep image reconstruction from human brain activity. PLoS Comput. Biol. 15 (2019)

    Google Scholar 

  7. Liu, C., Xie, S., Xie, X., Duan, X., Meng, Y.: Design of a video feedback SSVEP-BCI system for car control based on improved MUSIC method. In: International Conference on Brain and Computer Interface. GangWon, South Korea, p. 1 (2018)

    Google Scholar 

  8. Edelman, B.J., Meng, J., Suma, D., Zurn, C., Nagarajan, E., Baxter, B.S., Cline, C.C., He, B.: Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci. Robot. 4, w6844 (2019)

    Google Scholar 

  9. Zhang, M., Tang, Z., Liu, X., Van der Spiegel, J.: Electronic neural interfaces. Nat. Electron. 3, 191 (2020)

    Article  Google Scholar 

  10. Klimesch W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev. 29. 169 (1999).

    Google Scholar 

  11. EPOC X. https://www.emotiv.com/product/emotiv-epoc-x-14-channel-mobile-brainwear/

  12. EEG hardware platforms. http://neurosky.com/biosensors/eeg-sensor/biosensors/

  13. Liu, C., Yu, D., Zhang, J., Xie, S.: A utility human machine interface using low cost EEG cap and eye tracker. In: 2021 9th International Winter Conference on Brain-Computer Interface (BCI), vol. 1. IEEE, (2021)

    Google Scholar 

  14. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273 (1995)

    Google Scholar 

  15. Chen, X., Peng, X., Li, J., Peng, Y.: Overview of deep kernel learning based techniques and applications. J. Netw. Intell. 1, 83 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dingguo Yu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, C., Zhou, Y., Yu, D. (2022). Mind-Media System: A Consumer-Grade Brain-Computer Interface System for Media Applications. In: Zhang, JF., Chen, CM., Chu, SC., Kountchev, R. (eds) Advances in Intelligent Systems and Computing. Smart Innovation, Systems and Technologies, vol 268. Springer, Singapore. https://doi.org/10.1007/978-981-16-8048-9_8

Download citation

Publish with us

Policies and ethics