BRAIN-COMPUTER INTERFACE FOR THE AUGMENTATION OF BRAIN FUNCTIONS


Cite item

Full Text

Abstract

Brain-computer interface (BCI) connects the nervous system departments with external devices for the purpose of recovery of motor and sensory functions of patients with neurological lesions. Over the past half-century BCI have gone from initial ideas to the high-tech modern incarnations. This development contributed significantly to the invasive techniques of multichannel registration activity of neuronal ensembles. Modern BCI are able to manage mechanical prosthetic arms and legs. Furthermore, BCI can provide sensory feedback, allowing the user to feel the movement of the prosthesis and its interaction with external objects. Latest BCI connect multiple users to the brain network. In this review, these achievements are dealt with a focus on invasive BCI.

About the authors

M A Lebedev

Duke University (Durham, North Carolina, USA)

Email: lebedev@neuro.duke.edu
PhD (Physics and Mathematics), Senior Research Scientist, Department of Neurobiology, Duke University Medical Center (Durham, North Carolina, USA).

References

  1. Ramh y Cajal, S., Histology of the nervous system of man and vertebrates. History of neuroscience. 1995, New York: Oxford University Press
  2. Finger, S., Origins of neuroscience: a history of explorations into brain function. 1994, New York: Oxford University Press. XVIII, 462 p
  3. Adrian, E.D.A., The mechanism of nervous action; electrical studies of the neurone. Pennsylvania University The Eldridge Reeves Johnson foundation lectures 1931. 1932, Philadelphia, London: University of Pennsylvania Press; H. Milford, Oxford University Press. x, 103 p
  4. Shepherd, G.M., Foundations of the neuron doctrine. 25th anniversary edition. ed. 2016, Oxford; New York: Oxford University Press. XXVI, 339 pages
  5. Golgi, C., Sulla fina anatomia degli organi centrali del sistema nervoso. Biblioteca della scienza italiana. 1995, Firenze: Giunti. 264 p
  6. Connors, B.W. and M.A. Long, Electrical synapses in the mammalian brain. Annu Rev Neurosci, 2004. 27: 393-418
  7. Kosaka, T. and K. Hama, Gap junctions between non-pyramidal cell dendrites in the rat hippocampus (CA1 and CA3 regions): a combined Golgi-electron microscopy study. J Comp Neurol, 1985. 231(2):150-61
  8. Sloper, J.J., Gap junctions between dendrites in the primate neocortex. Brain Res, 1972. 44(2): 641-6
  9. Lewis, T.J. and J. Rinzel, Dynamics of spiking neurons connected by both inhibitory and electrical coupling. J Comput Neurosci, 2003. 14(3): 283-309
  10. Hebb, D.O., The organization of behavior : a neuropsychological theory. 2002, Mahwah, N.J.: L. Erlbaum Associates
  11. Georgopoulos, A.P., A.B. Schwartz, and R.E. Kettner, Neuronal population coding of movement direction. Science, 1986. 233(4771):1416-9
  12. Georgopoulos, A.P., Population activity in the control of movement. Int Rev Neurobiol, 1994. 37: p. 103-19; discussion 121-3
  13. Georgopoulos, A.P., R.E. Kettner, and A.B. Schwartz, Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J Neurosci, 1988. 8(8):2928-37
  14. Nicolelis, M.A., et al., Dynamic and distributed properties of many-neuron ensembles in the ventral posterior medial thalamus of awake rats. Proc Natl Acad Sci U S A, 1993. 90(6): 2212-6
  15. Nicolelis, M.A., et al., Simultaneous encoding of tactile information by three primate cortical areas. Nat Neurosci, 1998. 1(7): 621-30
  16. Nicolelis, M.A., et al., Reconstructing the engram: simultaneous, multisite, many single neuron recordings. Neuron, 1997. 18(4):529-37
  17. Wilson, M.A. and B.L. McNaughton, Reactivation of hippocampal ensemble memories during sleep. Science, 1994. 265(5172): 676-9
  18. Wilson, M.A. and B.L. McNaughton, Dynamics of the hippocampal ensemble code for space. Science, 1993. 261(5124): 1055-8
  19. Liao, L.D., et al., Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil, 2012. 9: 5
  20. Akhtar, A., et al., Playing checkers with your mind: an interactive multiplayer hardware game platform for brain-computer interfaces. Conf Proc IEEE Eng Med Biol Soc, 2014. 2014:1650-3
  21. Martisius, I. and R. Damasevicius, A Prototype SSVEP Based Real Time BCI Gaming System. Comput Intell Neurosci, 2016. 2016: p. 3861425
  22. Li, J., et al., A competitive brain computer interface: multiperson car racing system. Conf Proc IEEE Eng Med Biol Soc, 2013. 2013: 2200-3
  23. Sung, Y., K. Cho, and K. Um, A development architecture for serious games using BCI (brain computer interface) sensors. Sensors (Basel), 2012. 12(11): 15671-88
  24. Hasan, B.A. and J.Q. Gan, Hangman BCI: an unsupervised adaptive self-paced Brain-Computer Interface for playing games. Comput Biol Med, 2012. 42(5):598-606
  25. Finke, A., A. Lenhardt, and H. Ritter, The MindGame: a P300-based brain-computer interface game. Neural Netw, 2009. 22(9):1329-33
  26. Yang, L. and H. Leung, An online BCI game based on the decoding of users' attention to color stimulus. Conf Proc IEEE Eng Med Biol Soc, 2013. 2013: 5267-70
  27. Mason, S.G., et al., Real-time control of a video game with a direct brain--computer interface. J Clin Neurophysiol, 2004. 21(6): 404-8
  28. Ahn, M., et al., A review of brain-computer interface games and an opinion survey from researchers, developers and users. Sensors (Basel), 2014. 14(8):14601-33
  29. Garces Correa, A., L. Orosco, and E. Laciar, Automatic detection of drowsiness in EEG records based on multimodal analysis. Med Eng Phys, 2014. 36(2): 244-9
  30. Lin, C.T., et al., Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning. IEEE Trans Biomed Eng, 2008. 55(5): 1582-91
  31. Sahayadhas, A., K. Sundaraj, and M. Murugappan, Drowsiness detection during different times of day using multiple features. Australas Phys Eng Sci Med, 2013. 36(2): 243-50
  32. Liu, N.H., C.Y. Chiang, and H.M. Hsu, Improving driver alertness through music selection using a mobile EEG to detect brainwaves. Sensors (Basel), 2013. 13(7): 8199-221
  33. Picot, A., S. Charbonnier, and A. Caplier, On-line automatic detection of driver drowsiness using a single electroencephalographic channel. Conf Proc IEEE Eng Med Biol Soc, 2008. 2008: 3864-7
  34. Tsai, P.Y., et al., A portable device for real time drowsiness detection using novel active dry electrode system. Conf Proc IEEE Eng Med Biol Soc, 2009. 2009: 3775-8
  35. Chin-Teng, L., et al., A real-time wireless brain-computer interface system for drowsiness detection. IEEE Trans Biomed Circuits Syst, 2010. 4(4): 214-22
  36. Marchesi, M. and B. Ricc6. BRAVO: a brain virtual operator for education exploiting brain-computer interfaces. in CHI'13 Extended Abstracts on Human Factors in Computing Systems. 2013. ACM
  37. Maguire, G.Q. and E.M. McGee, Implantable brain chips? Time for debate. Hastings Center Report, 1999. 29(1):7-13
  38. McGee, E.M., Bioelectronics and Implanted devices, in Medical enhancement and posthumanity. 2008, Springer: 207-224
  39. Farah, M.J. and P.R. Wolpe, Monitoring and manipulating brain function: New neuroscience technologies and their ethical implications. Hastings Center Report, 2004. 34(3): 35-45
  40. Madan, C.R., Augmented memory: a survey of the approaches to remembering more. Frontiers in systems neuroscience, 2014. 8: 30
  41. Zehr, E.P., Future Think: Cautiously Optimistic About Brain Augmentation Using Tissue Engineering and Machine Interface. Frontiers in Systems Neuroscience, 2015
  42. Clausen, J., Moving minds: ethical aspects of neural motor prostheses. Biotechnology Journal, 2008. 3(12): 1493-1501
  43. Kamiya, J., Biofeedback training in voluntary control of EEG alpha rhythms. Calif Med, 1971. 115(3): 44
  44. Sterman, M.B., Neurophysiologic and clinical studies of sensorimotor EEG biofeedback training: some effects on epilepsy. Semin Psychiatry, 1973. 5(4): 507-25
  45. Weber, E.S., Autogenic training and EEG biofeedback training in coronary heart disease. J Med Soc N J, 1974. 71(12): 927-31
  46. Finley, W.W., H.A. Smith, and M.D. Etherton, Reduction of seizures and normalization of the EEG in a severe epileptic following sensorimotor biofeedback training: preliminary study. Biol Psychol, 1975. 2(3): 189-203
  47. Kaplan, B.J., Biofeedback in epileptics: equivocal relationship of reinforced EEG frequency to seizure reduction. Epilepsia, 1975. 16(3): 477-85
  48. Woodruff, D.S., Relationships among EEG alpha frequency, reaction time, and age: a biofeedback study. Psychophysiology, 1975. 12(6): 673-81
  49. Suter, S., Independent biofeedback self-regulation of EEG alpha and skin resistance. Biofeedback Self Regul, 1977. 2(3): 255-8
  50. Pelletier, K.R. and E. Peer, Developing a biofeedback model: alpha EEG feedback as a means for pain control. Int J Clin Exp Hypn, 1977. 25(4): 361-71
  51. Dahl, W.D., An alpha rhythm feedback control unit. Rep U S Nav Med Res Lab, 1962. 5848: 20
  52. Smith, K.U. and S.D. Ansell, Closed-Loop Digital Computer System for Study of Sensory Feedback Effects of Brain Rhythms. Am J Phys Med, 1965. 44: 125-37
  53. Nowlis, D.P. and J. Kamiya, The control of electroencephalographic alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology, 1970. 6(4): 476-84
  54. Nowlis, D.P. and E.C. Wortz, Control of the ratio of midline parietal to midline frontal EEG alpha rhythms through auditory feedback. Percept Mot Skills, 1973. 37(3): 815-24
  55. Sterman, M.B. and L. Friar, Suppression of seizures in an epileptic following sensorimotor EEG feedback training. Electroencephalogr Clin Neurophysiol, 1972. 33(1): 89-95
  56. Sterman, M.B., L.R. Macdonald, and R.K. Stone, Biofeedback training of the sensorimotor electroencephalogram rhythm in man: effects on epilepsy. Epilepsia, 1974. 15(3): 395-416
  57. Sterman, M.B., EEG biofeedback: physiological behavior modification. Neurosci Biobehav Rev, 1981. 5(3): 405-12
  58. Dennett, D.C., Consciousness explained. 1st ed. 1991, Boston: Little, Brown and Co. XIII, 511 p
  59. Walter, W.G., A machine that learns. Scientific American, 1951. 185(2): 60-63
  60. Walter, W.G., An imitation of life. Scientific American, 1950. 182(5): 42-45
  61. Frank, K., Some approaches to the technical problem of chronic excitation of peripheral nerve. Ann. Otol. Rhinol. Laryngol., 1968. 77:761-771
  62. Schmidt, E.M., Cortical Control of Robotic Devices And Neuromuscular Stimulators. Neurobionics: An Interdisciplinary Approach to Substitute Impaired Functions of the Human Nervous System, 2013: 289
  63. Humphrey, D.R., E.M. Schmidt, and W.D. Thompson, Predicting measures of motor performance from multiple cortical spike trains. Science, 1970. 170(3959): 758-62
  64. Schmidt, E.M., Single neuron recording from motor cortex as a possible source of signals for control of external devices. Ann Biomed Eng, 1980. 8(4-6): 339-49
  65. Fetz, E.E., Operant conditioning of cortical unit activity. Science, 1969. 163(3870): 955-8
  66. Brindley, G.S. and M.D. Craggs, The electrical activity in the motor cortex that accompanies voluntary movement. J Physiol, 1972. 223(1): 28P-29P
  67. Craggs, M.D., Electrical activity of the motor cortex associated with voluntary movements in the baboon. J Physiol, 1974. 237(2): 12P-13P
  68. Craggs, M.D., Cortical control of motor prostheses: using the cord-transected baboon as the primate model for human paraplegia. Adv Neurol, 1975. 10: 91-101
  69. Collins, W.R., Jr., F.E. Nulsen, and C.T. Randt, Relation of peripheral nerve fiber size and sensation in man. Arch Neurol, 1960. 3: 381-5
  70. Hensel, H. and K.K. Boman, Afferent impulses in cutaneous sensory nerves in human subjects. J Neurophysiol, 1960. 23: 564-78
  71. Brindley, G.S. and W.S. Lewin, The sensations produced by electrical stimulation of the visual cortex. J Physiol, 1968. 196(2): 479-93
  72. Brindley, G.S. and W.S. Lewin, The visual sensations produced by electrical stimulation of the medial occipital cortex. J Physiol, 1968. 194(2): 54-5P
  73. Libet, B., et al., Production of Threshold Levels of Conscious Sensation by Electrical Stimulation of Human Somatosensory Cortex. J Neurophysiol, 1964. 27: 546-78
  74. House, W.H., Cochlear implants. Ann Otol Rhinol Laryngol, 1976. 85(suppl 27): 3-91
  75. Djourno, A. and C. Eyries, Prothese auditive par excitation electrique a distance du nerf sensoriel a laide dun bobinage inclus a demeure. Presse me'dicale, 1957. 65(63): 1417
  76. Simmons, F.B., et al., Electrical Stimulation of Acoustical Nerve and Inferior Colliculus. Arch Otolaryngol, 1964. 79: 559-68
  77. Wilson, B.S. and M.F. Dorman, Cochlear implants: a remarkable past and a brilliant future. Hear Res, 2008. 242(1-2): 3-21
  78. Loizou, P.C., Introduction to cochlear implants. IEEE Eng Med Biol Mag, 1999. 18(1): 32-42
  79. Eddington, D.K., Speech discrimination in deaf subjects with cochlear implants. J Acoust Soc Am, 1980. 68(3): 885-91
  80. Eddington, D.K., Speech recognition in deaf subjects with multichannel intracochlear electrodes. Ann N Y Acad Sci, 1983. 405: 241-58
  81. Brindley, G.S., Sensations produced by electrical stimulation of the occipital poles of the cerebral hemispheres, and their use in constructing visual prostheses. Ann R Coll Surg Engl, 1970. 47(2): 106-8
  82. Dobelle, W.H., M.G. Mladejovsky, and J.P. Girvin, Artifical vision for the blind: electrical stimulation of visual cortex offers hope for a functional prosthesis. Science, 1974. 183(4123): 440-4
  83. Dobelle, W.H. and M.G. Mladejovsky, Phosphenes produced by electrical stimulation of human occipital cortex, and their application to the development of a prosthesis for the blind. J Physiol, 1974. 243(2): 553-76
  84. Dobelle, W.H., et al., "Braille" reading by a blind volunteer by visual cortex stimulation. Nature, 1976. 259(5539): 111-2
  85. Dobelle, W.H., et al., Artificial vision for the blind by electrical stimulation of the visual cortex. Neurosurgery, 1979. 5(4): 521-7
  86. Nicolelis, M.A., et al., Induction of immediate spatiotemporal changes in thalamic networks by peripheral block of ascending cutaneous information. Nature, 1993. 361(6412): 533-6
  87. Nicolelis, M.A., et al., Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system. Science, 1995. 268(5215): 1353-8
  88. Buzsaki, G., et al., Multisite recording of brain field potentials and unit activity in freely moving rats. J Neurosci Methods, 1989. 28(3): 209-17
  89. Chapin, J.K., et al., Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci, 1999. 2(7): 664-70
  90. Wessberg, J., et al., Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature, 2000. 408(6810): 361-5
  91. Vidal, J.-J., Toward direct brain-computer communication. Annual review of Biophysics and Bioengineering, 1973. 2(1): 157-180
  92. Bozinovski, S., M. Sestakov, and L. Bozinovska. Using EEG alpha rhythm to control a mobile robot. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1988
  93. Berger, H., Uber das elektrenkephalogramm des menschen. European Archives of Psychiatry and Clinical Neuroscience, 1929. 87(1): 527-570
  94. Niedermeyer, E., Alpha rhythms as physiological and abnormal phenomena. International Journal of Psychophysiology, 1997. 26(1-3): 31-49
  95. Birbaumer, N., et al., A spelling device for the paralysed. Nature, 1999. 398(6725): p. 297-8
  96. Taylor, D.M., S.I. Tillery, and A.B. Schwartz, Direct cortical control of 3D neuroprosthetic devices. Science, 2002. 296(5574): 1829-32
  97. Carmena, J.M., et al., Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol, 2003. 1(2): E42
  98. Serruya, M.D., et al., Instant neural control of a movement signal. Nature, 2002. 416(6877): 141-2
  99. Nordhausen, C.T., E.M. Maynard, and R.A. Normann, Single unit recording capabilities of a 100 microelectrode array. Brain Res, 1996. 726(1-2): 129-40
  100. Maynard, E.M., C.T. Nordhausen, and R.A. Normann, The Utah intracortical Electrode Array: a recording structure for potential brain-computer interfaces. Electroencephalogr Clin Neurophysiol, 1997. 102(3): 228-39
  101. Campbell, P.K., et al., A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array. Biomedical Engineering, IEEE Transactions on, 1991. 38(8):758-768
  102. Rousche, P.J. and R.A. Normann, A method for pneumatically inserting an array of penetrating electrodes into cortical tissue. Annals of biomedical engineering, 1992. 20(4): 413-422
  103. Kennedy, P.R., The cone electrode: a long-term electrode that records from neurites grown onto its recording surface. J Neurosci Methods, 1989. 29(3): 181-93
  104. Kennedy, P.R., R.A. Bakay, and S.M. Sharpe, Behavioral correlates of action potentials recorded chronically inside the Cone Electrode. Neuroreport, 1992. 3(7): 605-8
  105. Kennedy, P.R., S.S. Mirra, and R.A. Bakay, The cone electrode: ultrastructural studies following long-term recording in rat and monkey cortex. Neurosci Lett, 1992. 142(1): 89-94
  106. Guenther, F.H., et al., A wireless brain-machine interface for real-time speech synthesis. PLoS One, 2009. 4(12): e8218
  107. Brumberg, J.S., et al., Brain-Computer Interfaces for Speech Communication. Speech Commun, 2010. 52(4): 367-379
  108. Collinger, J.L., et al., High-performance neuroprosthetic control by an individual with tetraplegia. Lancet, 2013. 381(9866): 557-64
  109. Velliste, M., et al., Cortical control of a prosthetic arm for self-feeding. Nature, 2008. 453(7198): 1098-101
  110. Fitzsimmons, N.A., et al., Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity. Front Integr Neurosci, 2009. 3: 3
  111. Rajangam, S., et al., Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates. Sci Rep, 2016. 6: 22170
  112. Xu, Z., et al. On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity. in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. 2014. IEEE
  113. Bensmaia, S.J. and L.E. Miller, Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nature reviews Neuroscience, 2014. 15(5): 313-325
  114. O'Doherty, J.E., et al., Active tactile exploration using a brain-machine-brain interface. Nature, 201 1. 479(7372): 228-31
  115. O'Doherty, J.E., et al., A brain-machine interface instructed by direct intracortical microstimulation. Front Integr Neurosci, 2009. 3: 20
  116. Andersen, R.A., et al., Cognitive neural prosthetics. Trends Cogn Sci, 2004. 8(11): 486-93
  117. Berger, T.W., et al., A cortical neural prosthesis for restoring and enhancing memory. Journal of Neural Engineering, 2011. 8(4): 046017
  118. Fuchs, T., et al., Neurofeedback treatment for attentiondeficit/hyperactivity disorder in children: a comparison with methylphenidate. Applied psychophysiology and biofeedback, 2003. 28(1):1-12
  119. Lubar, J.F., Neurofeedback for the management of attention-deficit/hyperactivity disorders. 1995
  120. Musallam, S., et al., Cognitive control signals for neural prosthetics. Science, 2004. 305(5681): 258-262
  121. Ramakrishnan, A., et al., Computing Arm Movements with a Monkey Brainet. Sci Rep, 2015. 5: 10767
  122. Hochberg, L.R., et al., Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 2006. 442(7099): 164-71
  123. Hochberg, L.R., et al., Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 2012. 485(7398): 372-5
  124. Niedermeyer, E. and F.H. Lopes da Silva, Electroencephalography basic principles, clinical applications, and related fields. 2005, Lippincott Williams & Wilkins,: Philadelphia. p. 1 online resource XIII, 1309 p
  125. Taheri, B.A., R.T. Knight, and R.L. Smith, A dry electrode for EEG recording. Electroencephalogr Clin Neurophysiol, 1994. 90(5): 376-83
  126. Fonseca, C., et al., A novel dry active electrode for EEG recording. IEEE Trans Biomed Eng, 2007. 54(1): 162-5
  127. Chi, Y.M., T.P. Jung, and G. Cauwenberghs, Dry-contact and noncontact biopotential electrodes: methodological review. IEEE Rev Biomed Eng, 2010. 3:106-19
  128. Gargiulo, G., et al., Dry electrode bio-potential recordings. Conf Proc IEEE Eng Med Biol Soc, 2010. 2010: 6493-6
  129. Chi, Y.M., et al., Dry and noncontact EEG sensors for mobile brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2012. 20(2): 228-35
  130. Guger, C., et al., Comparison of dry and gel based electrodes for p300 brain-computer interfaces. Front Neurosci, 2012. 6: 60
  131. Lebedev, M.A. and M.A. Nicolelis, Brain-machine interfaces: past, present and future. Trends Neurosci, 2006. 29(9): 536-46
  132. Ifft, P.J., et al., A brain-machine interface enables bimanual arm movements in monkeys. Sci Transl Med, 2013. 5(210): p. 210ra154
  133. Wolpaw, J.R. and D.J. McFarland, Control of a twodimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U S A, 2004. 101(51): 17849-54
  134. Nicolelis, M.A. and M.A. Lebedev, Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nat Rev Neurosci, 2009. 10(7): 530-40
  135. Koralek, A.C., et al., Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature, 2012. 483(7389): 331-5
  136. Patil, P.G., et al., Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface. Neurosurgery, 2004. 55(1): p. 27-35; discussion 35-8
  137. Hanson, T.L., et al., Subcortical neuronal ensembles: an analysis of motor task association, tremor, oscillations, and synchrony in human patients. J Neurosci, 2012. 32(25): 8620-32
  138. Obermaier, B., et al., Information transfer rate in a five-classes brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2001. 9(3): 283-8
  139. Scherer, R., et al., The self-paced graz brain-computer interface: methods and applications. Comput Intell Neurosci, 2007: 79826
  140. Prasad, G., et al., Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J Neuroeng Rehabil, 2010. 7: p. 60
  141. Pfurtscheller, G. and C. Neuper, Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. Prog Brain Res, 2006. 159: 433-7
  142. Fazel-Rezai, R., et al., P300 brain computer interface: current challenges and emerging trends. Front Neuroeng, 2012. 5:14
  143. Lee, P.L., et al., An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system. Ann Biomed Eng, 2010. 38(7): 2383-97
  144. Wang, C., C. Guan, and H. Zhang, P300 brain-computer interface design for communication and control applications. Conf Proc IEEE Eng Med Biol Soc, 2005. 5: 5400-3
  145. Sellers, E.W., et al., A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol Psychol, 2006. 73(3): 242-52
  146. Donchin, E., K.M. Spencer, and R. Wijesinghe, The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil Eng, 2000. 8(2): 174-9
  147. Mirghasemi, H., R. Fazel-Rezai, and M.B. Shamsollahi, Analysis of p300 classifiers in brain computer interface speller. Conf Proc IEEE Eng Med Biol Soc, 2006. 1: 6205-8
  148. Piccione, F., et al., P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin Neurophysiol, 2006. 117(3): 531-7
  149. Townsend, G., et al., A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol, 2010. 121(7): 1109-20
  150. Brunner, P., et al., Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG). Front Neurosci, 2011. 5: 5
  151. Santhanam, G., et al., A high-performance brain-computer interface. Nature, 2006. 442(7099): 195-8
  152. Lebedev, M.A., et al., Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J Neurosci, 2005. 25(19): 4681-93
  153. Serruya, M., et al., Robustness of neuroprosthetic decoding algorithms. Biol Cybern, 2003. 88(3): 219-28
  154. Lebedev, M.A., J.E. O'Doherty, and M.A. Nicolelis, Decoding of temporal intervals from cortical ensemble activity. J Neurophysiol, 2008. 99(1): 166-86
  155. Lebedev, M.A. and M.A. Nicolelis, Toward a whole-body neuroprosthetic. Prog Brain Res, 2011. 194: 47-60
  156. Lebedev, M.A., How to read neuron-dropping curves? Front Syst Neurosci, 2014. 8: p. 102
  157. Fetz, E.E., Are movement parameters recognizably coded in the activity of single neurons? Behavioral and brain sciences, 1992. 15(04): 679-690
  158. Evarts, E.V., et al., Spontaneous discharge of single neurons during sleep and waking. Science, 1962. 135(3505): 726-8
  159. Evarts, E.V., Temporal Patterns of Discharge of Pyramidal Tract Neurons during Sleep and Waking in the Monkey. J Neurophysiol, 1964. 27: 152-71
  160. Evarts, E.V., Pyramidal tract activity associated with a conditioned hand movement in the monkey. J Neurophysiol, 1966. 29(6): 1011-27
  161. Kettner, R.E., A.B. Schwartz, and A.P. Georgopoulos, Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins. J Neurosci, 1988. 8(8): 2938-47
  162. Schwartz, A.B., R.E. Kettner, and A.P. Georgopoulos, Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement. J Neurosci, 1988. 8(8): 2913-27
  163. Batista, A.P., et al., Cortical neural prosthesis performance improves when eye position is monitored. IEEE Trans Neural Syst Rehabil Eng, 2008. 16(1): 24-31
  164. Musallam, S., et al., Cognitive control signals for neural prosthetics. Science, 2004. 305(5681): 258-62
  165. Ganguly, K. and J.M. Carmena, Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol, 2009. 7(7): e1000153
  166. Blazquez, P.M., et al., A network representation of response probability in the striatum. Neuron, 2002. 33(6): 973-82
  167. Montijn, J.S., M. Vinck, and C.M. Pennartz, Population coding in mouse visual cortex: response reliability and dissociability of stimulus tuning and noise correlation. Front Comput Neurosci, 2014. 8: 58
  168. Nicolelis, M.A.L., Beyond boundaries : the new neuroscience of connecting brains with machines--and how it will change our lives. 1st ed. 2011, New York: Times Books/ Henry Holt and Co. 353 p
  169. Fusi, S., E.K. Miller, and M. Rigotti, Why neurons mix: high dimensionality for higher cognition. Curr Opin Neurobiol, 2016. 37: 66-74
  170. Friston, K., J. Kilner, and L. Harrison, A free energy principle for the brain. J Physiol Paris, 2006. 100(1-3): 70-87
  171. Friston, K., The free-energy principle: a rough guide to the brain? Trends Cogn Sci, 2009. 13(7): 293-301
  172. Friston, K., The free-energy principle: a unified brain theory? Nat Rev Neurosci, 2010. 11(2): 127-38
  173. Tozzi, A., M. Zare, and A.A. Benasich, New perspectives on spontaneous brain activity: dynamic networks and energy matter. Frontiers in Human Neuroscience, 2016. 10
  174. Schwarz, D.A., et al., Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nat Methods, 2014. 11(6): 670-6
  175. Kralik, J.D., et al., Techniques for long-term multisite neuronal ensemble recordings in behaving animals. Methods, 2001. 25(2): 121-50
  176. Gualtierotti, T. and P. Bailey, A neutral buoyancy microelectrode for prolonged recording from single nerve units. Electroencephalography and clinical neurophysiology, 1968. 25(1): 77-81
  177. Musallam, S., et al., A floating metal microelectrode array for chronic implantation. Journal of neuroscience methods, 2007. 160(1): 122-127
  178. Neves, H., et al. Development of modular multifunctional probe arrays for cerebral applications. in Neural Engineering, 2007. CNE'07. 3rd International IEEE/EMBS Conference on. 2007. IEEE
  179. Spieth, S., et al., A floating 3D silicon microprobe array for neural drug delivery compatible with electrical recording. Journal of Micromechanics and Microengineering, 2011. 21(12): p. 125001
  180. Hassler, C., et al. Chronic intracortical implantation of saccharose-coated flexible shaft electrodes into the cortex of rats. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 2011. IEEE
  181. Kozai, T.D.Y. and D.R. Kipke, Insertion shuttle with carboxyl terminated self-assembled monolayer coatings for implanting flexible polymer neural probes in the brain. Journal of neuroscience methods, 2009. 184(2): 199-205
  182. Agorelius, J., et al., An array of highly flexible electrodes with a tailored configuration locked by gelatin during implantation - initial evaluation in cortex cerebri of awake rats. Frontiers in Neuroscience, 2015. 9
  183. Takeuchi, S., et al., 3D flexible multichannel neural probe array. Journal of micromechanics and microengineering, 2003. 14(1): p. 104
  184. Sohal, H.S., et al., The Sinusoidal Probe: a new approach to improve electrode longevity. Frontiers in Neuroengineering, 2014. 7
  185. Anderson, D.J., et al., Batch fabricated thin-film electrodes for stimulation of the central auditory system. Biomedical Engineering, IEEE Transactions on, 1989. 36(7): 693-704
  186. Najafi, K., K.D. Wise, and T. Mochizuki, A high-yield IC-compatible multichannel recording array. Electron Devices, IEEE Transactions on, 1985. 32(7): 1206-1211
  187. Vetter, R.J., et al., Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex. Biomedical Engineering, IEEE Transactions on, 2004. 51(6): 896-904
  188. Weiland, J.D. and D.J. Anderson, Chronic neural stimulation with thin-film, iridium oxide electrodes. Biomedical Engineering, IEEE Transactions on, 2000. 47(7): 911-918
  189. Recce, M. and J. O’keefe. The tetrode: a new technique for multi-unit extracellular recording. in Soc Neurosci Abstr. 1989
  190. Jog, M., et al., Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques. Journal of neuroscience methods, 2002. 117(2): 141-152
  191. Santos, L., et al., A novel tetrode microdrive for simultaneous multi-neuron recording from different regions of primate brain. Journal of neuroscience methods, 2012. 205(2): 368-374
  192. Seo, D., et al., Model validation of untethered, ultrasonic neural dust motes for cortical recording. J Neurosci Methods, 2015. 244: 114-22
  193. Llinas, R.R., et al., Neuro-vascular central nervous recording/stimulating system: Using nanotechnology probes. Journal of Nanoparticle Research, 2005. 7(2-3): 111-127
  194. Boniface, S. and N. Antoun, Endovascular electroencephalography: the technique and its application during carotid amytal assessment. Journal of Neurology, Neurosurgery & Psychiatry, 1997. 62(2): 193-195
  195. Oxley, T.J., et al., Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity. Nature biotechnology, 2016
  196. Tasaki, I., et al., Changes in fluorescence, turbidity, and birefringence associated with nerve excitation. Proceedings of the National Academy of Sciences of the United States of America, 1968. 61(3): 883
  197. Patrick, J., et al., Changes in extrinsic fluorescence intensity of the electroplax membrane during electrical excitation. The Journal of membrane biology, 1971. 5(1): 102-120
  198. Grinvald, A., et al., Optical imaging of neuronal activity. Physiological reviews, 1988. 68(4): 1285-1366
  199. Grinvald, A. and R. Hildesheim, VSDI: a new era in functional imaging of cortical dynamics. Nature Reviews Neuroscience, 2004. 5(11): 874-885
  200. Stosiek, C., et al., In vivo two-photon calcium imaging of neuronal networks. Proceedings of the National Academy of Sciences, 2003. 100(12): 7319-7324
  201. Smetters, D., A. Majewska, and R. Yuste, Detecting action potentials in neuronal populations with calcium imaging. Methods, 1999. 18(2): 215-221
  202. Grewe, B.F., et al., High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision. Nature methods, 2010. 7(5): 399-405
  203. Clancy, K.B., et al., Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat Neurosci, 2014. 17(6): 807-9
  204. Ziv, Y., et al., Long-term dynamics of CA1 hippocampal place codes. Nature neuroscience, 2013. 16(3): 264-266
  205. Crone, N.E., A. Sinai, and A. Korzeniewska, High-frequency gamma oscillations and human brain mapping with electrocorticography. Progress in brain research, 2006. 159: 275-295
  206. Leuthardt, E.C., et al., Electrocorticography-based brain computer interface - the Seattle experience. IEEE Trans Neural Syst Rehabil Eng, 2006. 14(2):194-8
  207. Hill, N.J., et al., Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. J Vis Exp, 2012(64)
  208. Miller, K.J., et al., Real-time functional brain mapping using electrocorticography. Neuroimage, 2007. 37(2): 504-507
  209. Viventi, J., et al., Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nature neuroscience, 2011. 14(12): 1599-1605
  210. Bleichner, M., et al., Give me a sign: decoding four complex hand gestures based on high-density ECoG. Brain Structure and Function, 2016. 221(1): 203-216
  211. Wang, W., et al. Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements. in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009. IEEE
  212. Harrison, R.R., et al., Wireless neural recording with single low-power integrated circuit. IEEE Trans Neural Syst Rehabil Eng, 2009. 17(4): 322-9
  213. Chestek, C.A., et al., HermesC: low-power wireless neural recording system for freely moving primates. IEEE Trans Neural Syst Rehabil Eng, 2009. 17(4): 330-8
  214. Chae, M., et al. A 128-channel 6mW wireless neural recording ic with on-the-fly spike sorting and uwb tansmitter. in 2008 IEEE International Solid-State Circuits Conference-Digest of Technical Papers. 2008. IEEE
  215. Kim, S., et al., Integrated wireless neural interface based on the Utah electrode array. Biomedical microdevices, 2009. 11(2): 453-466
  216. Szuts, T.A., et al., A wireless multi-channel neural amplifier for freely moving animals. Nature neuroscience, 2011. 14(2): 263-269
  217. Obeid, I., M.A. Nicolelis, and P.D. Wolf, A multichannel telemetry system for single unit neural recordings. J Neurosci Methods, 2004. 133(1-2): 33-8
  218. Mohseni, P., et al., Wireless multichannel biopotential recording using an integrated FM telemetry circuit. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005. 13(3): 263-271
  219. Morizio, J., et al. Wireless headstage for neural prosthetics. in Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005. 2005. IEEE
  220. Borghi, T., et al. A compact multichannel system for acquisition and processing of neural signals. in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2007. IEEE
  221. Kim, S.P., et al., A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces. J Neural Eng, 2006. 3(2): 145-61
  222. Wahnoun, R., J. He, and S.I.H. Tillery, Selection and parameterization of cortical neurons for neuroprosthetic control. Journal of neural engineering, 2006. 3(2):162
  223. Tkach, D., J. Reimer, and N.G. Hatsopoulos, Observation-based learning for brain-machine interfaces. Curr Opin Neurobiol, 2008. 18(6): 589-94
  224. Li, Z., et al., Adaptive decoding for brain-machine interfaces through Bayesian parameter updates. Neural Comput, 2011. 23(12): 3162-204
  225. Orsborn, A.L., et al., Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions. IEEE Trans Neural Syst Rehabil Eng, 2012. 20(4): 468-77
  226. Dangi, S., et al., Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Comput, 2014. 26(9): 1811-39
  227. Wessberg, J. and M.A. Nicolelis, Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys. J Cogn Neurosci, 2004. 16(6): 1022-35
  228. Georgopoulos, A.P., et al., Mental rotation of the neuronal population vector. Science, 1989. 243(4888): 234-6
  229. Ganguly, K., et al., Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat Neurosci, 2011. 14(5): 662-7
  230. Kalman, R.E., A new approach to linear filtering and prediction problems. Journal of basic Engineering, 1960. 82(1): 35-45
  231. Kalman, R.E. and R.S. Bucy, New results in linear filtering and prediction theory. Journal of basic engineering, 1961. 83(1): 95-108
  232. Serruya, M., A. Shaikhouni, and J. Donoghue. Neural decoding of cursor motion using a Kalman filter. in Advances in Neural Information Processing Systems 15: Proceedings of the 2002 Conference. 2003. MIT Press
  233. Wu, W., et al., Closed-loop neural control of cursor motion using a Kalman filter. Conf Proc IEEE Eng Med Biol Soc, 2004. 6: 4126-9
  234. Li, Z., et al., Unscented Kalman filter for brain-machine interfaces. PLoS One, 2009. 4(7): e6243
  235. Sussillo, D., et al., A recurrent neural network for closed-loop intracortical brain-machine interface decoders. Journal of neural engineering, 2012. 9(2): 026027
  236. Kowalski, K.C., B.D. He, and L. Srinivasan, Dynamic analysis of naive adaptive brain-machine interfaces. Neural Comput, 2013. 25(9): 2373-420
  237. Shanechi, M.M., et al., High-performance brain-machine interface enabled by an adaptive optimal feedback-controlled point process decoder. Conf Proc IEEE Eng Med Biol Soc, 2014. 2014: 6493-6
  238. Suminski, A.J., et al. Online adaptive decoding of intended movements with a hybrid kinetic and kinematic brain machine interface. in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2013. IEEE
  239. DiGiovanna, J., et al., Coadaptive brain-machine interface via reinforcement learning. IEEE transactions on biomedical engineering, 2009. 56(1): 54-64
  240. Mahmoudi, B., et al., Towards autonomous neuroprosthetic control using Hebbian reinforcement learning. Journal of neural engineering, 2013. 10(6): 066005
  241. Pohlmeyer, E.A., et al., Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization. PloS one, 2014. 9(1): e87253
  242. Sherrington, C.S., The integrative action of the nervous system. 1906, New York,: C. Scribner's sons. XVI, 411 p
  243. Guertin, P.A., The mammalian central pattern generator for locomotion. Brain Research Reviews, 2009. 62(1): 45-56
  244. Cordo, P.J. and V.S. Gurfinkel, Motor coordination can be fully understood only by studying complex movements. Progress in brain research, 2004. 143: 29-38
  245. Head, H. and G. Holmes, Sensory disturbances from cerebral lesions. Brain, 1911. 34(2-3): 102-254
  246. Kawato, M., Internal models for motor control and trajectory planning. Current opinion in neurobiology, 1999. 9(6): 718-727
  247. Wolpert, D.M., Z. Ghahramani, and M.I. Jordan, An internal model for sensorimotor integration. Science, 1995. 269(5232): 1880
  248. Feldman, A.G., Once more on the equilibrium-point hypothesis (X model) for motor control. Journal of motor behavior, 1986. 18(1): 17-54
  249. Feldman, A.G. and M.F. Levin, The equilibrium-point hypothesis-past, present and future, in Progress in motor control. 2009, Springer: 699-726
  250. Hochberg, L.R. and J.P. Donoghue, Sensors for brain-computer interfaces. IEEE Eng Med Biol Mag, 2006. 25(5): 32-8
  251. Cheng, G., et al. Bipedal locomotion with a humanoid robot controlled by cortical ensemble activity. in Abstr. Soc. Neurosci. 2007
  252. Kawato, M., Brain controlled robots. 2008
  253. Morrow, M.M. and L.E. Miller, Prediction of muscle activity by populations of sequentially recorded primary motor cortex neurons. Journal of neurophysiology, 2003. 89(4): 2279-2288
  254. Santucci, D.M., et al., Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates. Eur J Neurosci, 2005. 22(6): 1529-40
  255. Pohlmeyer, E.A., et al., Prediction of upper limb muscle activity from motor cortical discharge during reaching. Journal of neural engineering, 2007. 4(4): 369
  256. Seifert, H.M. and A.J. Fuglevand, Restoration of movement using functional electrical stimulation and Bayes' theorem. The journal of Neuroscience, 2002. 22(21): 9465-9474
  257. Johnson, L.A. and A.J. Fuglevand, Mimicking muscle activity with electrical stimulation. Journal of neural engineering, 2011. 8(1): 016009
  258. Pfurtscheller, J., et al., [Functional electrical stimulation instead of surgery? Improvement of grasping function with FES in a patient with C5 tetraplegia]. Unfallchirurg, 2005. 108(7): 587-90
  259. Pfurtscheller, G., et al., ‘Thought’-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience letters, 2003. 351(1): 33-36
  260. Moritz, C.T., S.I. Perlmutter, and E.E. Fetz, Direct control of paralysed muscles by cortical neurons. Nature, 2008. 456(7222): 639-42
  261. Ethier, C., et al., Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature, 2012. 485(7398): 368-371
  262. Pohlmeyer, E.A., et al., Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles. PloS one, 2009. 4(6): e5924
  263. Bouton, C.E., et al., Restoring cortical control of functional movement in a human with quadriplegia. Nature, 2016
  264. Cramer, S.C., et al., Harnessing neuroplasticity for clinical applications. Brain, 2011. 134(Pt 6):1591-609
  265. Dobkin, B.H., Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol, 2007. 579(Pt 3): 637-42
  266. Grosse-Wentrup, M., D. Mattia, and K. Oweiss, Using brain-computer interfaces to induce neural plasticity and restore function. Journal of neural engineering, 2011. 8(2): 025004
  267. Di Pino, G., et al., Augmentation-related brain plasticity. Frontiers in systems neuroscience, 2014. 8: 109
  268. Oweiss, K.G. and I.S. Badreldin, Neuroplasticity subserving the operation of brain-machine interfaces. Neurobiol Dis, 2015. 83:161-71
  269. Zacksenhouse, M., et al., Cortical modulations increase in early sessions with brain-machine interface. PLoS One, 2007. 2(7): e619
  270. Lebedev, M.A., et al., Future developments in brain-machine interface research. Clinics (Sao Paulo), 2011. 66 Suppl 1: 25-32
  271. Dobelle, W.H., Artificial vision for the blind. The summit may be closer than you think. ASAIO J, 1994. 40(4): 919-22
  272. Rothschild, R.M., Neuroengineering tools/applications for bidirectional interfaces, brain-computer interfaces, and neuroprosthetic implants - a review of recent progress. Front Neuroeng, 2010. 3:112
  273. Ghazanfar, A.A., D.J. Krupa, and M.A. Nicolelis, Role of cortical feedback in the receptive field structure and nonlinear response properties of somatosensory thalamic neurons. Exp Brain Res, 2001. 141(1):88-100
  274. Krupa, D.J., et al., Layer-specific somatosensory cortical activation during active tactile discrimination. Science, 2004. 304(5679):1989-92
  275. Pais-Vieira, M., et al., Simultaneous top-down modulation of the primary somatosensory cortex and thalamic nuclei during active tactile discrimination. J Neurosci, 2013. 33(9): 4076-93
  276. Lebedev, M.A., J.M. Denton, and R.J. Nelson, Vibration-entrained and premovement activity in monkey primary somatosensory cortex. J Neurophysiol, 1994. 72(4): 1654-73
  277. Gilbert, C.D. and M. Sigman, Brain states: top-down influences in sensory processing. Neuron, 2007. 54(5): 677-696
  278. Siegel, M., K.P. Kording, and P. Konig, Integrating top-down and bottom-up sensory processing by somato-dendritic interactions. Journal of computational neuroscience, 2000. 8(2): 161-173
  279. Cowey, A. and P. Stoerig, The neurobiology of blindsight. Trends in neurosciences, 1991. 14(4):140-145
  280. Stoerig, P. and A. Cowey, Blindsight in man and monkey. Brain, 1997. 120(3): 535-559
  281. Weiskrantz, L., Blindsight revisited. Current opinion in neurobiology, 1996. 6(2): 215-220
  282. Cowey, A. and P. Stoerig, Visual detection in monkeys with blindsight. Neuropsychologia, 1997. 35(7): 929-939
  283. Penfield, W. and E. Boldrey, Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain: A journal of neurology, 1937
  284. Romo, R., et al., Somatosensory discrimination based on cortical microstimulation. Nature, 1998. 392(6674): 387-390
  285. Fitzsimmons, N.A., et al., Primate reaching cued by multichannel spatiotemporal cortical microstimulation. J Neurosci, 2007. 27(21): 5593-602
  286. Tabot, G.A., et al., Restoring the sense of touch with a prosthetic hand through a brain interface. Proceedings of the National Academy of Sciences, 2013. 110(45): 18279-18284
  287. Pesaran, B., S. Musallam, and R.A. Andersen, Cognitive neural prosthetics. Curr Biol, 2006. 16(3): R77-80
  288. Ifft, P.J., M.A. Lebedev, and M.A. Nicolelis, Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change. Front Neuroeng, 2012. 5:16
  289. Pais-Vieira, M., et al., A brain-to-brain interface for real-time sharing of sensorimotor information. Sci Rep, 2013. 3:1319
  290. Yoo, S.-S., et al., Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains. PloS one, 2013. 8(4): e60410
  291. Rao, R.P., et al., A direct brain-to-brain interface in humans. PLoS One, 2014. 9(11): e111332

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2016 Lebedev M.A.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».