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BCI: Technologies and Applications Review and Toolkit Proposal

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Multimedia Communications, Services and Security (MCSS 2022)

Abstract

A typical example of a Brain-Computer Interface (BCI) is a system that allows a person to move a ball displayed on a computer screen to the left or to the right, simply by imagining the movement of the left or right hand, respectively. Since the term Brain-Computer Interface was coined in 1973, the interest and efforts in this field have grown tremendously and there are now thought to be several hundred laboratories worldwide developing research in this topic. This paper aims at summarizing its resulting knowledge in a way that allows for a quick and clear consultation, highlighting the research lines, technologies and the most relevant cases of applications, so that policy makers, professionals and consumers can make effective use of the findings. With this in mind, a Brain-Computer Interface toolkit is proposed with a focus on different target audiences (e.g., children, seniors, people with intellectual disabilities) that can take advantage of this resource and promote an independent life routine.

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Notes

  1. 1.

    Electrooculogram.

  2. 2.

    Electromyogram.

  3. 3.

    Heart rate.

  4. 4.

    Cerebral Vascular Accident.

  5. 5.

    Electroencephalogram.

  6. 6.

    Electrocardiogram.

References

  1. Vidal, J.J.: Toward direct brain-computer communication. Annu. Rev. Biophys. Bioeng. 2, 157–180 (1973)

    Article  Google Scholar 

  2. Nam, C.S., Nijholt, A., Lotte, F.: Brain-Computer Interfaces Handbook: Technological and Theoretical Advances, pp. 1–8. CRC Press (2018)

    Google Scholar 

  3. European Commission: Future BNCI: A Roadmap for Future Directions in Brain/Neuronal Computer Interaction (2012). http://bnci-horizon-2020.eu/images/bncih2020/FBNCI_Roadmap.pdf. Accessed 22 Feb 2021

  4. European Commission: BNCI Horizon 2020. http://www.bnci-horizon-2020.eu/about/basics. Accessed 12 Mar 2021

  5. Brunner, C., et al.: BNCI horizon 2020: towards a roadmap for the BCI community. BCI J. 1–10 (2015)

    Google Scholar 

  6. European Commission: The Future in Brain/Neural-Computer Interaction: Horizon 2020 (2015). http://www.bnci-horizon-2020.eu/images/bncih2020/Roadmap_BNCI_Horizon_2020.pdf. Accessed 24 Feb 2021

  7. Levac, D., Colquhoun, H., O’Brien, K.K.: Scoping studies: advancing the methodology. Implement. Sci. 5(1), 69 (2010)

    Article  Google Scholar 

  8. Wang, F., Zhang, X., Fu, R., Sun, G.: Study of the home-auxiliary robot based on BCI. Sens. Biosignal Process. 18(6), 1779 (2018)

    Google Scholar 

  9. Frolov, A.A., et al.: Preliminary results of a controlled study of BCI-exoskeleton technology efficacy in patients with poststroke arm paresis. Bull. RSMU (2) (2016)

    Google Scholar 

  10. Khan, M.J., Hong, K.-S.: Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to quadcopter control. Front. Neurorob. 11(6) (2017)

    Google Scholar 

  11. Stawicki, P., Gembler, F., Rezeika, A., Volosyak, I.: A novel hybrid mental spelling application based on eye tracking and SSVEP-based BCI. Brain Sci. 4(35) (2017)

    Google Scholar 

  12. Chaudhary, U., Birbaumer, N., Curado, M.R.: Brain-machine interface (BMI) in paralysis. Ann. Phys. Rehabil. Med. 58(1), 9–13 (2015)

    Article  Google Scholar 

  13. Alonso-Valerdi, L.M., Salido-Ruiz, R.A., Ramirez-Mendoza, R.A.: Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits. Neuropsychologia 79(B), 354–363 (2015)

    Article  Google Scholar 

  14. Ghube, C., Kulkarni, A., Bankar, C., Bedekar, M.: BMI Application: accident reduction using drowsiness detection. In: Abraham, A., Gandhi, N., Pant, M. (eds.) IBICA 2018. Advances in Intelligent Systems and Computing, vol. 939, pp. 66–72. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16681-6_7

    Chapter  Google Scholar 

  15. Yanagisawa, T., et al.: Induced sensorimotor brain plasticity controls pain in phantom limb patients. Nat. Commun. 7(13209) (2016)

    Google Scholar 

  16. Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., Volosyak, I.: 30-Targets Hybrid BNCI Speller Based on SSVEP and EMG. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan (2018)

    Google Scholar 

  17. Müller-Putz, G.R., et al.: Towards non-invasive EEG-based arm/hand-control in users with spinal cord injury. In: 2017 5th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South) (2017)

    Google Scholar 

  18. Chun, J., Bae, B., Jo, S.: BCI based hybrid interface for 3D object control in virtual reality. In: 2016 4th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South) (2016)

    Google Scholar 

  19. Spüler, M.: A brain-computer interface (BCI) system to use arbitrary Windows applications by directly controlling mouse and keyboard. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy (2015)

    Google Scholar 

  20. Han, X., Zhang, S., Gao, X.: A study on reducing training time of BCI system based on an SSVEP dynamic model. In: 2019 7th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South) (2019)

    Google Scholar 

  21. Tahir, M.N.: Wireless brain machine interface (BMI) system (review & concept). In: 2020 International Conference on Computing and Information Technology (ICCIT-1441), Tabuk, Saudi Arabia (2020)

    Google Scholar 

  22. Kim, H.-H., Jeong, J.: Representations of directions in EEG-BMI using winner-take-all readouts. In: 2017 5th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South) (2017)

    Google Scholar 

  23. Sarasola-Sanz, A., et al.: A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients. In: 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK (2017)

    Google Scholar 

  24. Rodriguez, R.J.: Electroencephalogram (EEG) based authentication leveraging visual evoked potentials (VEP) resulting from exposure to emotionally significant images. In: 2016 IEEE Symposium on Technologies for Homeland Security (HST), Waltham, MA, USA (2016)

    Google Scholar 

  25. Arpaia, P., Benedetto, E.D., Duraccio, L.: Design, implementation, and metrological characterization of a wearable, integrated AR-BCI hands-free system for health 4.0 monitoring. Measurement 177(109280) (2021)

    Google Scholar 

  26. Landau, O., Cohen, A., Gordon, S., Nissim, N.: Mind your privacy: privacy leakage through BCI applications using machine learning methods. Knowl.-Based Syst. 198(105932) (2020)

    Google Scholar 

  27. Lührs, M., et al.: The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution. NeuroImage 194, 228–243 (2019)

    Article  Google Scholar 

  28. Aricò, P., Borghini, G., Flumeri, G.D., Colosimo, A., Pozzi, S., Babiloni, F.: A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks. Prog. Brain Res. 228, 295–328 (2016)

    Article  Google Scholar 

  29. Mazzoleni, M., Previdi, F.: A comparison of classification algorithms for brain computer interface in drug craving treatment. IFAC-PapersOnLine 48(20), 487–492 (2015)

    Article  Google Scholar 

  30. Khalaf, A., Sybeldon, M., Sejdic, E., Akcakaya, M.: A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines. J. Neurosci. Methods 293, 174–182 (2018)

    Article  Google Scholar 

  31. Milsap, G., Collard, M., Coogan, C., Crone, N.E.: BCI2000Web and WebFM: browser-based tools for brain computer interfaces and functional brain mapping. Front. Neurosci. 12, 1030 (2019)

    Article  Google Scholar 

  32. Novak, D., et al.: Benchmarking brain-computer interfaces outside the laboratory: the Cybathlon 2016. Front. Neurosci. 11, 756 (2018)

    Article  Google Scholar 

  33. Medina-Juliá, M.T., Fernández-Rodríguez, Á., Velasco-Álvarez, F., Ron-Angevin, R.: P300-based brain-computer interface speller: usability evaluation of three speller sizes by severely motor-disabled patients. Front. Hum. Neurosci. 14(583358) (2020)

    Google Scholar 

  34. Liu, J., et al.: EEG-based emotion classification using a deep neural network and sparse autoencoder. Front. Syst. Neurosci. 14(43) (2020)

    Google Scholar 

  35. Matsushita, K., et al.: A fully implantable wireless ECoG 128-channel recording device for human brain-machine interfaces: W-HERBS. Front. Neurosci. 12(511) (2018)

    Google Scholar 

  36. Lühmann, A.V., Herff, C., Heger, D., Schultz, T.: Toward a wireless open source instrument: functional near-infrared spectroscopy in mobile neuroergonomics and BCI applications. Front. Hum. Neurosci. 9(617) (2015)

    Google Scholar 

  37. Zhang, J., Wang, B., Zhang, C., Xiao, Y., Wang, M.Y.: An EEG/EMG/EOG-based multimodal human-machine interface to real-time control of a soft robot hand. Front. Neurorob. 13(7) (2019)

    Google Scholar 

  38. https://scholar.google.com/. Accessed 26 July 2022

  39. https://ieeexplore.ieee.org/Xplore/home.jsp. Accessed 26 July 2022

  40. https://www.sciencedirect.com/. Accessed 26 July 2022

  41. https://www.frontiersin.org/. Accessed 26 July 2022

  42. https://www.bci2000.org. Accessed 26 July 2022

  43. http://biosig.sourceforge.net/index.html. Accessed 26 July 2022

  44. http://www.shifz.org/brainbay. Accessed 26 July 2022

  45. https://neuroimage.usc.edu/brainstorm. Accessed 26 July 2022

  46. https://sites.google.com/site/cartoolcommunity. Accessed 26 July 2022

  47. https://sccn.ucsd.edu/eeglab/index.php. Accessed 26 July 2022

  48. https://www.fieldtriptoolbox.org. Accessed 26 July 2022

  49. https://mne.tools/stable/index.html. Accessed 26 July 2022

  50. https://sites.google.com/view/fredm/home. Accessed 26 July 2022

  51. http://openvibe.inria.fr. Accessed 26 July 2022

  52. https://www.arduino.cc. Accessed 26 July 2022

  53. https://blog.arduino.cc/2011/01/07/arduino-the-documentary-now-online. Accessed 26 July 2022

  54. https://www.starcat.io/products/hackeeg-shield. Accessed 26 July 2022

  55. https://www.crowdsupply.com/starcat/hackeeg. Accessed 26 July 2022

  56. https://www.pluxbiosignals.com/collections/bitalino. Accessed 26 July 2022

  57. https://www.pluxbiosignals.com/collections/opensignals. Accessed 26 July 2022

  58. https://www.pluxbiosignals.com/pages/projects. Accessed 26 July 2022

  59. https://www.futurebehind.com/bitalino-pelo-mundo-plux-technologies. Accessed 26 July 2022

  60. https://tecnico.ulisboa.pt/pt/noticias/bitalino-a-conquista-do-mundo. Accessed 26 July 2022

  61. https://www.raspberrypi.com/products. Accessed 26 July 2022

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Acknowledgments

This work was supported by the RD Project “Continental Factory of Future, (CONTINENTAL FoF) / POCI-01- 0247-FEDER-047512”, financed by the European Regional Development Fund (ERDF), through the Program “Programa Operacional Competitividade e Internacionalização (POCI) / PORTUGAL 2020”, under the management of AICEP Portugal Global – Trade Investment Agency.

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Correspondence to Tânia Rocha .

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Rocha, T., Carvalho, D., Letra, P., Reis, A., Barroso, J. (2022). BCI: Technologies and Applications Review and Toolkit Proposal. In: Dziech, A., Mees, W., Niemiec, M. (eds) Multimedia Communications, Services and Security. MCSS 2022. Communications in Computer and Information Science, vol 1689. Springer, Cham. https://doi.org/10.1007/978-3-031-20215-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-20215-5_11

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