Brain-Computer Interface and Neurofeedback Technologies: Current State, Problems and Clinical Prospects (Review)

Brain-computer interface and neurofeedback technologies are unique techniques to modulate brain activity based on an operant conditioning. From the time these technologies appeared in the 60-ies of the XX c., they have become non-drug tools for numerous psychiatric and neurologic disorders. However, up to now their efficiency is a matter of debate. Our review considers the background, characteristic features and current state of the technologies. The emphasis was made on the analysis of capabilities and prospects of the technologies in clinical medicine to mobilize the plasticity mechanisms of brain neural network. The review presents the findings of our own experiments showing the future of brain-computer interface and neurofeedback technologies to depend on multi-type cooperation of neurologists, neurobiologists, engineers and mathematicians. Effective consolidation of several fields of science will enable to develop novel therapeutic regimens to restore and improve neural, cognitive and behavioral functions.

1. Kaplan A.Ya., Kochetova A.G., Shishkin S.L., Basyul I.A., Ganin I.P., Vasilev A.N., Liburkina S.P. Experimental and theoretical foundations and practical implementation of technology brain-computer interface. Byulleten’ sibirskoy meditsiny 2013; 12(2): 21–29.
2. Arns M., Heinrich H., Ros T., Rothenberger A., Strehl U. Editorial: neurofeedback in ADHD. Front Hum Neurosci 2015; 9: 602, https://doi.org/10.3389/fnhum.2015.00602.
3. Frederick J.A. Psychophysics of EEG alpha state discrimination. Conscious Cogn 2012; 21(3): 1345–1354, https://doi.org/10.1016/j.concog.2012.06.009.
4. Choi K. Electroencephalography (EEG)-based neurofeedback training for brain–computer interface (BCI). Exp Brain Res 2013; 231(3): 351–365, https://doi.org/10.1007/s00221-013-3699-6.
5. Huster R.J., Mokom Z.N., Enriquez-Geppert S., Herrmann C.S. Brain–computer interfaces for EEG neurofeedback: peculiarities and solutions. Int J Psychophysiol 2014; 91(1): 36–45, https://doi.org/10.1016/j.ijpsycho.2013.08.011.
6. Wood G., Kober S.E., Witte M., Neuper C. On the need to better specify the concept of “control” in brain-computer-interfaces/neurofeedback research. Front Syst Neurosci 2014; 8: 171, https://doi.org/10.3389/fnsys.2014.00171.
7. Johnston S.J., Boehm S.G., Healy D., Goebel R., Linden D.E.J. Neurofeedback: a promising tool for the self-regulation of emotion networks. NeuroImage 2010; 49(1): 1066–1072, https://doi.org/10.1016/j.neuroimage.2009.07.056.
8. Lofthouse N., Arnold L.E., Hurt E. Current status of neurofeedback for attention-deficit/hyperactivity disorder. Curr Psychiatry Rep 2012; 14(5): 536–542, https://doi.org/10.1007/s11920-012-0301-z.
9. Nicolas-Alonso L.F., Gomez-Gil J. Brain computer interfaces, a review. Sensors 2012; 12(12): 1211–1279, https://doi.org/10.3390/s120201211.
10. Minyaeva N.R. Noninvasive technologies in brain computer interface systems. Valeologiya 2012; 4: 29–31.
11. Arns M., Heinrich H., Strehl U. Evaluation of neurofeedback in ADHD: the long and winding road. Biol Psychol 2014; 95: 108–115, https://doi.org/10.1016/j.biopsycho.2013.11.013.
12. Gevensleben H., Moll G.H., Rothenberger A., Heinrich H. Neurofeedback in attention-deficit/hyperactivity disorder — different models, different ways of application. Front Hum Neurosci 2014; 8: 846, https://doi.org/10.3389/fnhum.2014.00846.
13. Holtmann M., Sonuga-Barke E., Cortese S., Brandeis D. Neurofeedback for ADHD: a review of current evidence. Child Adolesc Psychiatr Clin N Am 2014; 23(4): 789–806, https://doi.org/10.1016/j.chc.2014.05.006.
14. Hurt E., Arnold L.E., Lofthouse N. Quantitative EEG neurofeedback for the treatment of pediatric attention-deficit/hyperactivity disorder, autism spectrum disorders, learning disorders, and epilepsy. Child Adolesc Psychiatr Clin N Am 2014; 23(3): 465–486, https://doi.org/10.1016/j.chc.2014.02.001.
15. Linden D.E. Neurofeedback and networks of depression. Dialogues Clin Neurosci 2014; 16(1): 103–112.
16. Micoulaud-Franchi J.A., Geoffroy P.A., Fond G., Lopez R., Bioulac S., Philip P. EEG neurofeedback treatments in children with ADHD: an updated meta-analysis of randomized controlled trials. Front Hum Neurosci 2014; 8: 906, https://doi.org/10.3389/fnhum.2014.00906.
17. Strehl U. What learning theories can teach us in designing neurofeedback treatments. Front Hum Neurosci 2014; 8: 894, https://doi.org/10.3389/fnhum.2014.00894.
18. Wander J.D., Rao R.P. Brain–computer interfaces: a powerful tool for scientific inquiry. Curr Opin Neurobiol 2014; 25: 70–75, https://doi.org/10.1016/j.conb.2013.11.013.
19. Shurkhay V.A., Aleksandrova E.V., Potapov A.A., Goryaynov S.A. The current state of the brain-computer interface problem. Voprosy neyrokhirurgii im. N.N. Burdenko 2015; 79(1): 97–104.
20. Huggins J.E., Moinuddin A.A., Chiodo A.E., Wren P.A. What would brain-computer interface users want: opinions and priorities of potential users with spinal cord injury. Arch Phys Med Rehabil 2015; 96(3): S38–S45.e5, https://doi.org/10.1016/j.apmr.2014.05.028.
21. Peters B., Bieker G., Heckman S.M., Huggins J.E., Wolf C., Zeitlin D., Fried-Oken M. Brain-computer interface users speak up: the Virtual Users’ Forum at the 2013 International Brain-Computer Interface Meeting. Arch Phys Med Rehabil 2015; 96(3 Suppl): S33–S37, https://doi.org/10.1016/j.apmr.2014.03.037.
22. Jensen M.P., Sherlin L.H., Askew R.L., Fregni F., Witkop G., Gianas A., Howe J.D., Hakimian S. Effects of non-pharmacological pain treatments on brain states. Clin Neurophysiol 2013; 124(10): 2016–2024, https://doi.org/10.1016/j.clinph.2013.04.009.
23. Larsen S., Sherlin L. Neurofeedback: an emerging technology for treating central nervous system dysregulation. Psychiatr Clin North Am 2013; 36(1): 163–168, https://doi.org/10.1016/j.psc.2013.01.005.
24. Meisel V., Servera M., Garcia-Banda G., Cardo E., Moreno I. Neurofeedback and standard pharmacological intervention in ADHD: a randomized controlled trial with six-month follow-up. Biol Psychol 2013; 94(1): 12–21, https://doi.org/10.1016/j.biopsycho.2013.04.015.
25. Holtmann M., Pniewski B., Wachtlin D., Wörz S., Strehl U. Neurofeedback in children with attention-deficit/hyperactivity disorder (ADHD) — a controlled multicenter study of a non-pharmacological treatment approach. BMC Pediatr 2014; 14(1): 202, https://doi.org/10.1186/1471-2431-14-202.
26. Johnson M.R. Fear of stimulant therapy for children and adolescents with attention-deficit/hyperactivity disorder. J Child Adolesc Psychopharmacol 2015; 25(2): 182, https://doi.org/10.1089/cap.2014.0117.
27. Haller S., Kopel R., Jhooti P., Haas T., Scharnowski F., Lovblad K.O., Scheffler K., Van De Ville D. Dynamic reconfiguration of human brain functional networks through neurofeedback. Neuroimage 2013; 81: 243–252, https://doi.org/10.1016/j.neuroimage.2013.05.019.
28. Burns A., Adeli H., Buford J.A. Brain–computer interface after nervous system injury. Neuroscientist 2014; 20(6): 639–651, https://doi.org/10.1177/1073858414549015.
29. Bamdad M., Zarshenas H., Auais M.A. Application of BCI systems in neurorehabilitation: a scoping review. Disabil Rehabil Assist Technol 2015; 10(5): 355–364, https://doi.org/10.3109/17483107.2014.961569.
30. Bowsher K., Civillico E.F., Coburn J., Collinger J., Contreras-Vidal J.L., Denison T., Donoghue J., French J., Getzoff N., Hochberg L.R., Hoffmann M., Judy J., Kleitman N., Knaack G., Krauthamer V., Ludwig K., Moynahan M., Pancrazio J.J., Peckham P.H., Pena C., Pinto V., Ryan T., Saha D., Scharen H., Shermer S., Skodacek K., Takmakov P., Tyler D., Vasudevan S., Wachrathit K., Weber D., Welle C.G., Ye M. Brain–computer interface devices for patients with paralysis and amputation: a meeting report. J Neural Eng 2016; 13(2): 023001, https://doi.org/10.1088/1741-2560/13/2/023001.
31. Daly J.J., Huggins J.E. Brain–computer interface: current and emerging rehabilitation applications. Arch Phys Med Rehabil 2015; 96(3): S1–S7, https://doi.org/10.1016/j.apmr.2015.01.007.
32. Vidal J.J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng 1973; 2(1): 157–180, https://doi.org/10.1146/annurev.bb.02.060173.001105.
33. Gurfinkel’ V.S., Malkin V.B., Tsetlin M.L., Shneyder A.Yu. Bioelektricheskoe upravlenie [Bioelectric control]. Moscow: Nauka; 1972; 244 p.
34. Bekhtereva N.P., Usov V.V. The technique of intermittent photostimulation in the rhythm of brain self potentials in ECG recording. Fiziologicheskiy zhurnal SSSR im. I.M. Sechenova 1960; 46(1): 108–111.
35. Kumano H., Horie H., Shidara T., Kuboki T., Suematsu H., Yasushi M. Treatment of a depressive disorder patient with EEG-driven photic stimulation. Biofeedback Self Regul 1996; 21(4): 323–334, https://doi.org/10.1007/bf02214432.
36. Kamei T., Toriumi Y., Kumano H., Fukada M., Matsumoto T. Use of photic feedback as an adjunct treatment in a case of miller fisher syndrome. Percept Mot Skills 2000; 90(1): 262–264, https://doi.org/10.2466/pms.90.1.262-264.
37. Woertz M., Pfurtscheller G., Klimesch W. Alpha power dependent light stimulation: dynamics of event-related (de)synchronization in human electroencephalogram. Brain Res Cogn Brain Res 2004; 20(2): 256–260, https://doi.org/10.1016/j.cogbrainres.2004.03.014.
38. Shih J.J., Krusienski D.J., Wolpaw J.R. Brain-computer interfaces in medicine. Mayo Clin Proc 2012; 87(3): 268–279, https://doi.org/10.1016/j.mayocp.2011.12.008.
39. Yanagisawa T., Hirata M., Saitoh Y., Kishima H., Matsushita K., Goto T., Fukuma R., Yokoi H., Kamitani Y., Yoshimine T. Electrocorticographic control of a prosthetic arm in paralyzed patients. Ann Neurol 2011; 71(3): 353–361, https://doi.org/10.1002/ana.22613.
40. Frolov A.A., Biryukova E.V., Bobrov P.D., Mokienko O.A., Platonov A.K., Pryanichnikov V.E., Chernikova L.A. Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton. Hum Physiol 2013; 39(2): 196–208, https://doi.org/10.1134/s0362119713020035.
41. Kaplan A.Y. The harmony of explosion: an interview. Otechestvennye zapiski 2014; 2(59): 123–136.
42. Levitskaya O.S., Lebedev M.A. Brain-computer interface: the future in the present. Vestnik Rossiyskogo gosudarstvennogo meditsinskogo universiteta 2016; 2: 4–16.
43. Hebert J.S., Olson J.L., Morhart M.J., Dawson M.R., Marasco P.D., Kuiken T.A., Chan K.M. Novel targeted sensory reinnervation technique to restore functional hand sensation after transhumeral amputation. IEEE Trans Neural Syst Rehabil Eng 2014; 22(4): 765–773, https://doi.org/10.1109/tnsre.2013.2294907.
44. Kwok R. Neuroprosthetics: once more, with feeling. Nature 2013; 497(7448): 176–178, https://doi.org/10.1038/497176a.
45. Miranda R.A., Casebeer W.D., Hein A.M., Judy J.W., Krotkov E.P., Laabs T.L., Manzo J.E., Pankratz K.G., Pratt G.A., Sanchez J.C., Weber D.J., Wheeler T.L., Ling G.S. DARPA-funded efforts in the development of novel brain–computer interface technologies. J Neurosci Methods 2015; 244: 52–67, https://doi.org/10.1016/j.jneumeth.2014.07.019.
46. Ganin I.P., Shishkin S.L., Kaplan A.Y. A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design. PLoS One 2013; 8(10): e77755, https://doi.org/10.1371/journal.pone.0077755.
47. Mokienko O.A., Lyukmanov R.K., Chernikova L.A., Suponeva N.A., Piradov M.A., Frolov A.A. Brain–computer interface: the first experience of clinical use in Russia. Hum Physiol 2016; 42(1): 24–31, https://doi.org/10.1134/s0362119716010126.
48. Frolov A.A., Mokienko O.A., Lyukmanov R.Kh., Chernikova L.A., Kotov S.V., Turbina L.G., Bobrov P.D., Biryukova E.V., Kondur A.A., Ivanova G.E., Staritsyn A.N., Bushkova Yu.V., Dzhalagoniya I.Z., Kurganskaya M.E., Pavlova O.G., Budilin S.Yu., Aziatskaya G.A., Khizhnikova A.E., Chervyakov A.V., Lukyanov A.L., Nadareyshvily G.G. Preliminary results of a controlled study of BCI-exoskeleton technology efficacy in patients with poststroke arm paresis. Vestnik Rossiyskogo gosudarstvennogo meditsinskogo universiteta 2016; 2: 17–25.
49. Frolov A.A., Husek D., Silchenko A.V., Tintera J., Rydlo J. The changes in the hemodynamic activity of the brain during motor imagery training with the use of brain-computer interface. Hum Physiol 2016; 42(1): 1–12, https://doi.org/10.1134/s0362119716010084.
50. Shishkin S.L., Kozyrskiy B.L., Trofimov A.G., Nuzhdin Y.O., Federova A.A., Svirin E.P., Velichkovsky B.M. Improving eye–brain–computer interface performance by using electroencephalogram frequency components. Vestnik Rossiyskogo gosudarstvennogo meditsinskogo universiteta 2016; 2: 39–44.
51. Bradberry T.J., Gentili R.J., Contreras-Vidal J.L. Fast attainment of computer cursor control with noninvasively acquired brain signals. J Neural Eng 2011; 8(3): 036010, https://doi.org/10.1088/1741-2560/8/3/036010.
52. Kaplan A.Y. Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation. Hum Physiol 2016; 42(1): 103–110, https://doi.org/10.1134/s0362119716010102.
53. Hammond D.C. What is neurofeedback: an update. J Neurother 2011; 15(4): 305–336, https://doi.org/10.1080/10874208.2011.623090.
54. Kamiya J. The first communications about operant conditioning of the EEG. J Neurother 2011; 15(1): 65–73, https://doi.org/10.1080/10874208.2011.545764.
55. Ghaziri J., Tucholka A., Larue V., Blanchette-Sylvestre M., Reyburn G., Gilbert G., Lévesque J., Beauregard M. Neurofeedback training induces changes in white and gray matter. Clin EEG Neurosci 2013; 44(4): 265–272, https://doi.org/10.1177/1550059413476031.
56. Seitz A.R. Cognitive Neuroscience: Targeting neuroplasticity with neural decoding and biofeedback. Curr Biol 2013; 23(5): R210–R212, https://doi.org/10.1016/j.cub.2013.01.015.
57. Aslanyan E.V., Kiroy V.N., Stoletniy A.S., Lazurenko D.M., Bahtin O.M., Minyaeva N.R., Kiroy R.I. Impact of individual personality features on ability to voluntary regulation of expression EEG alpha and beta frequencies. Rossiyskiy fiziologicheskiy zhurnal im. I.M. Sechenova 2015; 101(5): 599–613.
58. Kiroy V.N., Lazurenko D.M., Shepelev I.E., Minyaeva N.R., Aslanyan E.V., Bakhtin O.M., Shaposhnikov D.G., Vladimirskiy B.M. Changes in EEG spectral characteristics in the course of neurofeedback training. Hum Physiol 2015; 41(3): 269–279, https://doi.org/10.1134/s0362119715030081.
59. Niv S. Clinical efficacy and potential mechanisms of neurofeedback. Pers Individ Dif 2013; 54(6): 676–686, https://doi.org/10.1016/j.paid.2012.11.037.
60. Ros T., Baars J.B., Lanius R.A., Vuilleumier P. Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. Front Hum Neurosci 2014; 8: 1008, https://doi.org/10.3389/fnhum.2014.01008.
61. Arnold L.E., Lofthouse N., Hersch S., Pan X., Hurt E., Bates B., Kassouf K., Moone S., Grantier C. EEG neurofeedback for ADHD: double-blind sham-controlled randomized pilot feasibility trial. J Atten Disord 2012; 17(5): 410–419, https://doi.org/10.1177/1087054712446173.
62. Gevensleben H., Kleemeyer M., Rothenberger L.G., Studer P., Flaig-Röhr A., Moll G.H., Rothenberger A., Heinrich H. Neurofeedback in ADHD: further pieces of the puzzle. Brain Topogr 2014; 27(1): 20–32, https://doi.org/10.1007/s10548-013-0285-y.
63. Escolano C., Navarro-Gil M., Garcia-Campayo J., Congedo M., Minguez J. The effects of individual upper alpha neurofeedback in ADHD: an open-label pilot study. Appl Psychophysiol Biofeedback 2014; 39(3–4): 193–202, https://doi.org/10.1007/s10484-014-9257-6.
64. Duric N.S., Aßmus J., Elgen I.B. Self-reported efficacy of neurofeedback treatment in a clinical randomized controlled study of ADHD children and adolescents. Neuropsychiatr Dis Treat 2014; 10: 1645–1645, https://doi.org/10.2147/ndt.s66466.
65. Bink M., van Nieuwenhuizen C., Popma A., Bongers I.L., van Boxtel G.J.M. Neurocognitive effects of neurofeedback in adolescents with ADHD. J Clin Psychiatry 2014; 75(05): 535–542, https://doi.org/10.4088/jcp.13m08590.
66. Cannon R.L., Pigott H.E., Surmeli T., Simkin D.R., Thatcher R.W., Van den Bergh W., Gluck G., Lubar J.F., Davis R., Foster D.S., Douglas J., Malcolm A.T., Bars D., Little K., Center W., Berman M., Russell H., Hammer B., Koberda J.L. The problem of patient heterogeneity and lack of proper training in a study of EEG neurofeedback in children. J Clin Psychiatry 2014; 75(3): 289–290, https://doi.org/10.4088/jcp.13lr08850.
67. Lee Y.-S., Bae S.-H., Lee S.-H., Kim K.-Y. Neurofeedback training improves the dual-task performance ability in stroke patients. Tohoku J Exp Med 2015; 236(1): 81–88, https://doi.org/10.1620/tjem.236.81.
68. Peskind E.R., Brody D., Cernak I., McKee A., Ruff R.L. Military- and sports-related mild traumatic brain injury. J Clin Psychiatry 2013; 74(8): e17, https://doi.org/10.4088/jcp.12011nr2c.
69. Strehl U., Birkle S.M., Wörz S., Kotchoubey B. Sustained reduction of seizures in patients with intractable epilepsy after self-regulation training of slow cortical potentials — 10 years after. Front Hum Neurosci 2014; 8: 604, https://doi.org/10.3389/fnhum.2014.00604.
70. Ross S.M. Neurofeedback. Holist Nurs Pract 2013; 27(4): 246–250, https://doi.org/10.1097/hnp.0b013e3182971b7c.
71. Unterrainer H.F., Lewis A.J., Gruzelier J.H. EEG-neurofeedback in psychodynamic treatment of substance dependence. Front Psychol 2013; 4: 692, https://doi.org/10.3389/fpsyg.2013.00692.
72. Dehghani-Arani F., Rostami R., Nadali H. Neurofeedback training for opiate addiction: improvement of mental health and craving. Appl Psychophysiol Biofeedback 2013; 38(2): 133–141, https://doi.org/10.1007/s10484-013-9218-5.
73. Peeters F., Oehlen M., Ronner J., van Os J., Lousberg R. Neurofeedback as a treatment for major depressive disorder — a pilot study. PLoS One 2014; 9(3): e91837, https://doi.org/10.1371/journal.pone.0091837.
74. Pineda J.A., Juavinett A., Datko M. Self-regulation of brain oscillations as a treatment for aberrant brain connections in children with autism. Med Hypotheses 2012; 79(6): 790–798, https://doi.org/10.1016/j.mehy.2012.08.031.
75. Sorokina N.D., Selitskii G.V. Tension headache and migraine: efficacy of biological feed-back in their treatment. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 2013; 113(4): 86–91.
76. Bartholdy S., Musiat P., Campbell I.C., Schmidt U. The potential of neurofeedback in the treatment of eating disorders: a review of the literature. Eur Eat Disord Rev 2013; 21(6): 456–463, https://doi.org/10.1002/erv.2250.
77. Jensen M.P., Day M.A., Miró J. Neuromodulatory treatments for chronic pain: efficacy and mechanisms. Nat Rev Neurol 2014; 10(3): 167–178, https://doi.org/10.1038/nrneurol.2014.12.
78. Hassan M.A., Fraser M., Conway B.A., Allan D.B., Vuckovic A. The mechanism of neurofeedback training for treatment of central neuropathic pain in paraplegia: a pilot study. BMC Neurol 2015; 15(1), https://doi.org/10.1186/s12883-015-0445-7.
79. Schoenberg P.L.A., David A.S. Biofeedback for psychiatric disorders: a systematic review. Appl Psychophysiol Biofeedback 2014; 39(2): 109–135, https://doi.org/10.1007/s10484-014-9246-9.
80. Farkas A., Bluschke A., Roessner V., Beste C. Neurofeedback and its possible relevance for the treatment of Tourette syndrome. Neurosci Biobehav Rev 2015; 51: 87–99, https://doi.org/10.1016/j.neubiorev.2015.01.012.
81. Graczyk M., Pąchalska M., Ziółkowski A., Mańko G., Łukaszewska B., Kochanowicz K., Mirski A., Kropotov I.D. Neurofeedback training for peak performance. Ann Agric Environ Med 2014; 21(4): 871–875, https://doi.org/10.5604/12321966.1129950.
82. Ruiz S., Birbaumer N., Sitaram R. Editorial: learned brain self-regulation for emotional processing and attentional modulation: from theory to clinical applications. Front Behav Neurosci 2016; 10, https://doi.org/10.3389/fnbeh.2016.00062.
83. Hayashibe M., Guiraud D., Pons J.L., Farina D. Editorial: biosignal processing and computational methods to enhance sensory motor neuroprosthetics. Front Neurosci 2015; 9: 434, https://doi.org/10.3389/fnins.2015.00434.
84. Thibault R.T., Lifshitz M., Raz A. The self-regulating brain and neurofeedback: experimental science and clinical promise. Cortex 2016; 74: 247–261, https://doi.org/10.1016/j.cortex.2015.10.024.
85. Hammond D.C. The need for individualization in neurofeedback: heterogeneity in QEEG patterns associated with diagnoses and symptoms. Appl Psychophysiol Biofeedback 2009; 35(1): 31–36, https://doi.org/10.1007/s10484-009-9106-1.
86. Lazareva O.Yu., Bazanova O.M. The effects of instructions on the efficiency of EEG alpha power voluntary increase training. Byulleten’ sibirskoy meditsiny 2013; 12(2): 58–65.
87. Sokhadze E.M., El-Baz A.S., Tasman A., Sears L.L., Wang Y., Lamina E.V., Casanova M.F. Neuromodulation integrating rTMS and neurofeedback for the treatment of autism spectrum disorder: an exploratory study. Appl Psychophysiol Biofeedback 2014; 39(3–4): 237–257, https://doi.org/10.1007/s10484-014-9264-7.
88. Tang H.-Y., Vitiello M.V., Perlis M., Riegel B. Open-Loop neurofeedback audiovisual stimulation: a pilot study of its potential for sleep induction in older adults. Appl Psychophysiol Biofeedback 2015; 40(3): 183–188, https://doi.org/10.1007/s10484-015-9285-x.
89. Fedotchev A.I. Efficacy of EEG biofeedback procedures in correcting stress-related functional disorders. Hum Physiol 2010; 36(1): 86–90, https://doi.org/10.1134/s0362119710010111.
90. Gruzelier J.H. EEG-neurofeedback for optimising performance. III: a review of methodological and theoretical considerations. Neurosci Biobehav Rev 2014; 44: 159–182, https://doi.org/10.1016/j.neubiorev.2014.03.015.
91. Friedrich E.V., Suttie N., Sivanathan A., Lim T., Louchart S., Pineda J.A. Brain-computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum. Front Neuroeng 2014; 7: 21, https://doi.org/10.3389/fneng.2014.00021.
92. Fedotchev A.I., Oh S.J., Semikin G.I. Combination of neurofeedback technique with music therapy for effective correction of stress-induced disorders. Sovremennye tehnologii v medicine 2014; 6(3): 60–63.
93. Fedotchev A.I., Bondar A.T., Bakhchina A.V., Grigorieva V.N., Katayev A.A., Parin S.B., Radchenko G.S., Polevaya S.A. Transformation of patient’s EEG oscillators into music-like signals for correction of stress-induced functional states. Sovremennye tehnologii v medicine 2016; 8(1): 93–98, https://doi.org/10.17691/stm2016.8.1.12.
94. Fedotchev A.I. Analysis of resonance EEG reactions in estimating the effectiveness of sensor exposure. Fiziologiya cheloveka 1997; 23(4): 117–123.
95. Fedotchev A.I., Bondar A.T., Akoev I.G. Human EEG spectral structure investigation: modern state of the art and the tendencies. Uspekhi fiziologicheskikh nauk 2000; 31(3): 39–53.
96. Fedotchev A.I., Bondar' A.T., Matrusov S.G., Semenov V.S., Soin A.G. Utilization of feedback signals from patient’s own endogenous rhythms for non-drug correction of human functional disturbances. Uspekhi fiziologicheskikh nauk 2006; 37(4): 82–93.
97. Koelsch S. Brain correlates of music-evoked emotions. Nat Rev Neurosci 2014; 15(3): 170–180, https://doi.org/10.1038/nrn3666.
98. Thoma M.V., La Marca R., Brönnimann R., Finkel L., Ehlert U., Nater U.M. The effect of music on the human stress response. PLoS One 2013; 8(8): e70156, https://doi.org/10.1371/journal.pone.0070156.
99. Radstaak M., Geurts S.A.E., Brosschot J.F., Kompier M.A.J. Music and psychophysiological recovery from stress. Psychosom Med 2014; 76(7): 529–537, https://doi.org/10.1097/psy.0000000000000094.
100. Rollnik J.D., Altenmüller E. Music in disorders of consciousness. Front Neurosci 2014; 8, https://doi.org/10.3389/fnins.2014.00190.
101. Clark C.N., Downey L.E., Warren J.D. Brain disorders and the biological role of music. Soc Cogn Affect Neurosci 2014; 10(3): 444–452, https://doi.org/10.1093/scan/nsu079.
102. Gray E. In practice: music: a therapy for all? Perspect Public Health 2013; 133(1): 14, https://doi.org/10.1177/1757913912468642.
103. Höller Y., Thomschewski A., Schmid E.V., Höller P., Crone J.S., Trinka E. Individual brain-frequency responses to self-selected music. Int J Psychophysiol 2012; 86(3): 206–213, https://doi.org/10.1016/j.ijpsycho.2012.09.005.
104. Park M., Hennig-Fast K., Bao Y., Carl P., Pöppel E., Welker L., Reiser M., Meindl T., Gutyrchik E. Personality traits modulate neural responses to emotions expressed in music. Brain Res 2013; 1523: 68–76, https://doi.org/10.1016/j.brainres.2013.05.042.
105. Müller W., Haffelder G., Schlotmann A., Schaefers A.T.U., Teuchert-Noodt G. Amelioration of psychiatric symptoms through exposure to music individually adapted to brain rhythm disorders — a randomised clinical trial on the basis of fundamental research. Cogn Neuropsychiatry 2014; 19(5): 399–413, https://doi.org/10.1080/13546805.2013.879054.
106. Fedotchev A.I., Bondar’ A.T., Bakhchina A.V., Parin S.B., Polevaya S.A., Radchenko G.S. Music-acoustic signals controlled by subject’s brain potentials in the correction of unfavorable functional states. Uspekhi fiziologicheskikh nauk 2016; 47(1): 69–79.
107. Fedotchev A.I., Kim E.V. Correction of functional disturbances during pregnancy by the method of adaptive EEG biofeedback training. Hum Physiol 2006; 32(6): 652–656, https://doi.org/10.1134/s0362119706060041.
108. Fedotchev A.I., Kim E.V. Peculiarities of biocontrol treatment sessions with electroencephalogram feedback in physiological and aggravated pregnancies. Zhurnal vysshey nervnoy deyatel’nosti im. I.P. Pavlova 2009; 59(4): 421–428.
109. Fedotshev A.I. Stress, the consequences of its influence on humans and modern non-drug methods of stress-induced states reduction. Uspekhi fiziologicheskikh nauk 2009; 40(1): 77–91.
110. Fedotchev A.I., Zemlyanaya A.A., Polevaya S.A., Savchuk L.V. Attention deficit hyperactivity disorder and current possibilities of its treatment by the method of neurofeedback training. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 2016; 116(5): 98, https://doi.org/10.17116/jnevro20161165198-101.

Fedotchev А.I., Parin S.B., Polevaya S.A., Velikova S.D. Brain-Computer Interface and Neurofeedback Technologies: Current State, Problems and Clinical Prospects (Review). Sovremennye tehnologii v medicine 2017; 9(1): 175, https://doi.org/10.17691/stm2017.9.1.22