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Brain-Computer Interfaces for Controlling Unmanned Aerial Vehicles: Computational Tools for Cognitive Training

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Biologically Inspired Cognitive Architectures 2019 (BICA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 948))

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Abstract

Attention deficit-hyperactivity disorder is considered a mental disease that affects a significant number of the world’s youth population. Brain-computer interfaces have been used to study and treat this mental disease. In this paper, we present the current state of unmanned aerial vehicles controlled by mental commands. We hope this study can be useful to guide future research focused to develop brain-computer interfaces able of controlling unmanned aerial vehicles for therapeutic purposes.

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References

  1. Shidhaye R, Lund C, Chisholm D (2015) Closing the treatment gap for mental, neurological and substance use disorders by strengthening existing health care platforms: strategies for delivery and integration of evidence-based interventions. Int J Ment Health Syst 9(1):1–11. https://doi.org/10.1186/s13033-015-0031-9

    Article  Google Scholar 

  2. Patel V, Xiao S, Chen H, Hanna F, Jotheeswaran A, Luo D, Parikh R, Sharma E, Usmani S, Yu Y (2016) The magnitude of and health system responses to the mental health treatment gap in adults in India and China. Lancet 388(10063):3074–3084. https://doi.org/10.1016/S0140-6736(16)00160-4

    Article  Google Scholar 

  3. Kazdin AE (2017) Addressing the treatment gap: a key challenge for extending evidence-based psychosocial interventions. Behav Res Ther 88:7–18. https://doi.org/10.1016/j.brat.2016.06.004

    Article  Google Scholar 

  4. Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, Rivera DE, Spring B, Michie S, Asch DA (2015) Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med 5(3):335–346. https://doi.org/10.1007/s13142-015-0324-1

    Article  Google Scholar 

  5. Silva BM, Rodrigues JJ, de la Torre Díez I, López-Coronado M, Saleem K (2015) Mobile-health: a review of current state in 2015. J Biomed Inform 56:265–272. https://doi.org/10.1016/j.jbi.2015.06.003

    Article  Google Scholar 

  6. Naslund JA, Aschbrenner KA, Barre LK, Bartels SJ (2015) Feasibility of popular m-health technologies for activity tracking among individuals with serious mental illness. Telemed E-Health 21(3):213–216. https://doi.org/10.1089/tmj.2014.0105

    Article  Google Scholar 

  7. Formolo D, Van Ments L, Treur J (2017) A computational model to simulate development and recovery of traumatised patients. Biol Inspired Cogn Arch 21:26–36. https://doi.org/10.1016/j.bica.2017.07.002

    Article  Google Scholar 

  8. Yu Y, He D, Hua W, Li S, Qi Y, Wang Y, Pan G (2012) Flying-Buddy2: a brain-controlled assistant for the handicapped. In: UbiComp. ACM, pp 669–670

    Google Scholar 

  9. Scott JE, Scott CH (2018) Models for drone delivery of medications and other healthcare items. Int J Healthc Inf Syst Inform (IJHISI) 13(3):20–34. https://doi.org/10.4018/IJHISI.2018070102

    Article  Google Scholar 

  10. Van de Voorde P, Gautama S, Momont A, Ionescu C, De Paepe P, Fraeyman N (2017) The drone ambulance [A-UAS]: golden bullet or just a blank? Resuscitation 116:46–48. https://doi.org/10.1016/j.resuscitation.2017.04.037

    Article  Google Scholar 

  11. Polanczyk G, De Lima MS, Horta BL, Biederman J, Rohde LA (2007) The world-wide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 164:942–948

    Article  Google Scholar 

  12. Hammond DC (2011) What is neurofeedback: an update. J Neurother 15(4):305–336. https://doi.org/10.1080/10874208.2011.623090

    Article  Google Scholar 

  13. Bearden TS, Cassisi JE, Pineda M (2003) Neurofeedback training for a patient with thalamic and cortical infarctions. Appl Psychophysiol Biofeedback 28(3):241–253. https://doi.org/10.1023/A:1024689315563

    Article  Google Scholar 

  14. Tinius TP, Tinius KA (2000) Changes after EEG biofeedback and cognitive retraining in adults with mild traumatic brain injury and attention deficit hyperactivity disorder. J Neurother 4(2):27–44. https://doi.org/10.1300/J184v04n02_05

    Article  Google Scholar 

  15. Fernández T, Bosch-Bayard J, Harmony T, Caballero MI, Díaz-Comas L, Galán L, Ricardo-Garcell J, Aubert E, Otero-Ojeda G (2016) Neurofeedback in learning disabled children: visual versus auditory reinforcement. Appl Psychophysiol Biofeedback 41(1):27–37. https://doi.org/10.1007/s10484-015-9309-6

    Article  Google Scholar 

  16. Van Doren J, Arns M, Heinrich H, Vollebregt MA, Strehl U, Loo SK (2018) Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis. Eur Child Adolesc Psychiatry 1–13. https://doi.org/10.1007/s00787-018-1121-4

    Article  Google Scholar 

  17. Johnstone SJ, Roodenrys SJ, Johnson K, Bonfield R, Bennett SJ (2017) Game-based combined cognitive and neurofeedback training using focus pocus reduces symptom severity in children with diagnosed AD/HD and subclinical AD/HD. Int J Psychophysiol 116:32–44. https://doi.org/10.1016/j.ijpsycho.2017.02.015

    Article  Google Scholar 

  18. Silvagni M, Tonoli A, Zenerino E, Chiaberge M (2017) Multipurpose UAV for search and rescue operations in mountain avalanche events. Geomat Nat Hazards Risk 8:18–33. https://doi.org/10.1080/19475705.2016.1238852

    Article  Google Scholar 

  19. Choi I, Rhiu I, Lee Y, Yun MH, Nam CS (2017) A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives. PLoS ONE 12:e0176674. https://doi.org/10.1371/journal.pone.0176674

    Article  Google Scholar 

  20. Kosmyna N, Tarpin-Bernard F, Rivet B (2015) Towards brain computer interfaces for recreational activities: piloting a drone. In: Human-computer interaction. Springer, pp 506–522. https://doi.org/10.1007/978-3-319-22701-6_37

    Chapter  Google Scholar 

  21. Shi T, Wang H, Zhang C (2015) Brain computer interface system based on indoor semi-autonomous navigation and motor imagery for unmanned aerial vehicle control. Expert Syst Appl 42:4196–4206. https://doi.org/10.1016/j.eswa.2015.01.031

    Article  Google Scholar 

  22. Lin JS, Jiang ZY (2015) Implementing remote presence using quadcopter control by a non-invasive BCI device. Comput Sci Inf Technol 3:122–126. https://doi.org/10.13189/csit.2015.030405

    Article  Google Scholar 

  23. Kim BH, Kim M, Jo S (2014) Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking. Comput Biol Med 51:82–92. https://doi.org/10.1016/j.compbiomed.2014.04.020

    Article  Google Scholar 

  24. Khan MJ, Hong KS, Naseer N, Bhutta MR (2015) Hybrid EEG-NIRS based BCI for quadcopter control. In: 54th annual conference of the society of instrument and control engineers of Japan (SICE). IEEE, Hangzhou, pp 1177–1182. https://doi.org/10.1109/sice.2015.7285434

  25. Khan MJ, Hong KS (2017) Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to quadcopter control. Front Neurorobotics 11:1–13. https://doi.org/10.3389/fnbot.2017.00006

    Article  Google Scholar 

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Authors and Affiliations

  1. Centro Universitario de los Valles, 46600, Ameca, Mexico

    Sonia López, José-Antonio Cervantes, Salvador Cervantes & Jahaziel Molina

  2. Instituto Tecnológico y de Estudios Superiores de Occidente, 45604, Tlaquepaque, Mexico

    Francisco Cervantes

Authors
  1. Sonia López
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  2. José-Antonio Cervantes
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  3. Salvador Cervantes
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  4. Jahaziel Molina
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  5. Francisco Cervantes
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Corresponding author

Correspondence to José-Antonio Cervantes .

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Editors and Affiliations

  1. Moscow Engineering Physics Institute (MEPhI), Department of Cybernetics, National Research Nuclear University (NRNU), Moscow, Russia

    Alexei V. Samsonovich

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López, S., Cervantes, JA., Cervantes, S., Molina, J., Cervantes, F. (2020). Brain-Computer Interfaces for Controlling Unmanned Aerial Vehicles: Computational Tools for Cognitive Training. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_40

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