CoChat
Sign Up
Cited by 35EEG and Brain-Computer InterfacesSleep and related disordersSleep and Wakefulness Research

A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies—Related Issues and Future Directions

Open access
Choi, Jinyoung, Kwon, Moonyoung, Jun, Sung Chan·2020·Sensors
DOI 10.3390/s20102770PMC PMC7285770
Open this paper in CoChat — free accountOpen PDF

Abstract

Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep’s effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with ‘sleep and stimulation’ and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s20102770

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 110

  1. [1]Potter S.M. El Hady A. Fetz E.E. Closed-loop neuroscience and neuroengineering Front. Neural Circuits 2014 8 10.3389/fncir.2014.00115 25294988 PMC4171982 · DOI
  2. [2]Siegle J.H. Wilson M.A. Enhancement of encoding and retrieval functions through theta phase-specific manipulation of hippocampus Elife 2014 3 10.7554/eLife.03061 25073927 PMC4384761 · DOI
  3. [3]Opris I. Fuqua J.L. Huettl P.F. Gerhardt G.A. Berger T.W. Hampson R.E. Deadwyler S.A. Closing the loop in primate prefrontal cortex: Inter-laminar processing Front. Neural Circuits 2012 6 10.3389/fncir.2012.00088 23189041 PMC3504312 · DOI
  4. [4]Buzsaki G. Stark E. Berenyi A. Khodagholy D. Kipke D.R. Yoon E. Wise K.D. Tools for Probing Local Circuits: High-Density Silicon Probes Combined with Optogenetics Neuron 2015 86 92 105 10.1016/j.neuron.2015.01.028 25856489 PMC4392339 · DOI
  5. [5]Grosenick L. Marshel J.H. Deisseroth K. Closed-Loop and Activity-Guided Optogenetic Control Neuron 2015 86 106 139 10.1016/j.neuron.2015.03.034 25856490 PMC4775736 · DOI
  6. [6]Gomez-Rodriguez M. Peters J. Hill J. Scholkopf B. Gharabaghi A. Grosse-Wentrup M. Closing the sensorimotor loop: Haptic feedback facilitates decoding of motor imagery J. Neural Eng. 2011 8 10.1088/1741-2560/8/3/036005 21474878 · DOI
  7. [7]Vukelic M. Gharabaghi A. Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality Neuroimage 2015 111 1 11 10.1016/j.neuroimage.2015.01.058 25665968 · DOI
  8. [8]Bauer R. Gharabaghi A. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: A Bayesian simulation Front. Neurosci. Switz. 2015 9 10.3389/fnins.2015.00036 PMC4325901 25729347 · DOI
  9. [9]Grimm F. Walter A. Spuler M. Naros G. Rosenstiel W. Gharabaghi A. Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton Front. Neurosci. Switz. 2016 10 10.3389/fnins.2016.00367 PMC4977295 27555805 · DOI
  10. [10]Little S. Pogosyan A. Neal S. Zavala B. Zrinzo L. Hariz M. Foltynie T. Limousin P. Ashkan K. Fitzgerald J. Adaptive deep brain stimulation in advanced Parkinson disease Ann. Neurol. 2013 74 449 457 10.1002/ana.23951 23852650 PMC3886292 · DOI · Paper page
  11. [11]Little S. Tan H.L. Anzak A. Pogosyan A. Kuhn A. Brown P. Bilateral Functional Connectivity of the Basal Ganglia in Patients with Parkinson’s Disease and Its Modulation by Dopaminergic Treatment PLoS ONE 2013 8 e82762 10.1371/journal.pone.0082762 24376574 PMC3869733 · DOI
  12. [12]Little S. Beudel M. Zrinzo L. Foltynie T. Limousin P. Hariz M. Neal S. Cheeran B. Cagnan H. Gratwicke J. Bilateral adaptive deep brain stimulation is effective in Parkinson’s disease J. Neurol. Neurosur. Psychiatry 2016 87 717 721 10.1136/jnnp-2015-310972 26424898 PMC4941128 · DOI
  13. [13]Bauer R. Fels M. Royter V. Raco V. Gharabaghi A. Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation Clin. Neurophysiol. 2016 127 3156 3164 10.1016/j.clinph.2016.06.020 27474965 · DOI
  14. [15]Ahn M. Lee M. Choi J. Jun S.C. A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users Sensors 2014 14 14601 14633 10.3390/s140814601 25116904 PMC4178978 · DOI
  15. [16]Kandel A. Buzsáki G. Cellular–Synaptic Generation of Sleep Spindles, Spike-and-Wave Discharges, and Evoked Thalamocortical Responses in the Neocortex of the Rat J. Neurosci. 1997 17 6783 6797 10.1523/JNEUROSCI.17-17-06783.1997 9254689 PMC6573130 · DOI
  16. [19]Saebipour M.R. Joghataei M.T. Yoonessi A. Sadeghniiat-Haghighi K. Khalighinejad N. Khademi S. Slow oscillating transcranial direct current stimulation during sleep has a sleep-stabilizing effect in chronic insomnia: A pilot study J. Sleep Res. 2015 24 518 525 10.1111/jsr.12301 26014344 · DOI
  17. [20]Bellesi M. Riedner B.A. Garcia-Molina G.N. Cirelli C. Tononi G. Enhancement of sleep slow waves: Underlying mechanisms and practical consequences Front. Syst. Neurosci. 2014 8 10.3389/fnsys.2014.00208 PMC4211398 25389394 · DOI · Paper page
  18. [21]Ngo H.-V.V. Claussen J.C. Born J. Mölle M. Induction of slow oscillations by rhythmic acoustic stimulation J. Sleep Res. 2013 22 22 31 10.1111/j.1365-2869.2012.01039.x 22913273 · DOI
  19. [22]Goel N. Kim H. Lao R.P. An olfactory stimulus modifies nighttime sleep in young men and women Chronobiol. Int. 2005 22 889 904 10.1080/07420520500263276 16298774 · DOI
  20. [23]Perl O. Arzi A. Sela L. Secundo L. Holtzman Y. Samnon P. Oksenberg A. Sobel N. Hairston I.S. Odors enhance slow-wave activity in non-rapid eye movement sleep J. Neurophysiol. 2016 115 2294 2302 10.1152/jn.01001.2015 26888107 PMC4922455 · DOI
  21. [24]Arzi A. Holtzman Y. Samnon P. Eshel N. Hare E. Sobel N. Olfactory Aversive Conditioning during Sleep Reduces Cigarette-Smoking Behavior J. Neurosci. 2014 34 15382 15393 10.1523/JNEUROSCI.2291-14.2014 25392505 PMC6608450 · DOI
  22. [25]Krystal A.D. Zammit G.K. Wyatt J.K. Quan S.F. Edinger J.D. White D.P. Chiacchierini R.P. Malhotra A. The Effect of Vestibular Stimulation in a Four-Hour Sleep Phase Advance Model of Transient Insomnia J. Clin. Sleep Med. 2010 6 315 321 10.5664/jcsm.27871 20726278 PMC2919660 · DOI
  23. [26]Bayer L. Constantinescu I. Perrig S. Vienne J. Vidal P.P. Muhlethaler M. Schwartz S. Rocking synchronizes brain waves during a short nap Curr. Biol. 2011 21 R461 R462 10.1016/j.cub.2011.05.012 21683897 · DOI
  24. [27]Omlin X. Crivelli F. Naf M. Heinicke L. Skorucak J. Malafeev A. Guerrero A.F. Riener R. Achermann P. The Effect of a Slowly Rocking Bed on Sleep Sci. Rep. UK 2018 8 10.1038/s41598-018-19880-3 PMC5794757 29391413 · DOI
  25. [28]Pereira S.I.R. Beijamini F. Weber F.D. Vincenzi R.A. Da Silva F.A.C. Louzada F.M. Tactile stimulation during sleep alters slow oscillation and spindle densities but not motor skill Physiol. Behav. 2017 169 59 68 10.1016/j.physbeh.2016.11.024 27887994 · DOI
  26. [29]Schalk G. McFarland D.J. Hinterberger T. Birbaumer N. Wolpaw J.R. BCI2000: A general-purpose, brain-computer interface (BCI) system IEEE Trans Biomed. Eng. 2004 51 1034 1043 10.1109/TBME.2004.827072 15188875 · DOI
  27. [31]Kothe C.A. Makeig S. BCILAB: A platform for brain-computer interface development J. Neural Eng. 2013 10 10.1088/1741-2560/10/5/056014 23985960 · DOI · Paper page
  28. [32]Patel Y.A. George A. Dorval A.D. White J.A. Christini D.J. Butera R.J. Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI) PLoS Comput. Biol. 2017 13 10.1371/journal.pcbi.1005430 PMC5469488 28557998 · DOI
  29. [33]Newman J.P. Zeller-Townson R. Fong M.F. Desai S.A. Gross R.E. Potter S.M. Closed-loop, multichannel experimentation using the open-source NeuroRighter electrophysiology platform Front. Neural Circuits 2013 6 10.3389/fncir.2012.00098 PMC3548271 23346047 · DOI
  30. [34]Ciliberti D. Kloosterman F. Falcon: A highly flexible open-source software for closed-loop neuroscience J. Neural Eng. 2017 14 10.1088/1741-2552/aa7526 28548044 · DOI
  31. [35]Moher D. Liberati A. Tetzlaff J. Altman D.G. Group P. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement J. Clin. Epidemiol. 2009 62 1006 1012 10.1016/j.jclinepi.2009.06.005 19631508 · DOI · Paper page
  32. [36]Ngo H.-V. Martinetz T. Born J. Mölle M. Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory Neuron 2013 78 545 553 10.1016/j.neuron.2013.03.006 23583623 · DOI · Paper page
  33. [37]Ngo H.V. Miedema A. Faude I. Martinetz T. Molle M. Born J. Driving sleep slow oscillations by auditory closed-loop stimulation-a self-limiting process J. Neurosci. 2015 35 6630 6638 10.1523/JNEUROSCI.3133-14.2015 25926443 PMC4412888 · DOI
  34. [38]Santostasi G. Malkani R. Riedner B. Bellesi M. Tononi G. Paller K.A. Zee P.C. Phase-locked loop for precisely timed acoustic stimulation during sleep J. Neurosci. Methods 2016 259 101 114 10.1016/j.jneumeth.2015.11.007 26617321 PMC5169172 · DOI
  35. [39]Ong J.L. Lo J.C. Chee N.I.Y.N. Santostasi G. Paller K.A. Zee P.C. Chee M.W.L. Effects of phase-locked acoustic stimulation during a nap on EEG spectra and declarative memory consolidation Sleep Med. 2016 20 88 97 10.1016/j.sleep.2015.10.016 27318231 · DOI
  36. [41]Leminen M.M. Virkkala J. Saure E. Paajanen T. Zee P.C. Santostasi G. Hublin C. Muller K. Porkka-Heiskanen T. Huotilainen M. Enhanced Memory Consolidation Via Automatic Sound Stimulation During Non-REM Sleep Sleep 2017 40 10.1093/sleep/zsx003 28364428 PMC5806588 · DOI
  37. [42]Choi S.H. Yoon H. Jin H.W. Kwon H.B. Oh S.M. Lee Y.J. Park K.S. Effect of Closed-Loop Vibration Stimulation on Heart Rhythm during Naps Sensors 2019 19 4136 10.3390/s19194136 31554268 PMC6806257 · DOI
  38. [44]Sharon O. Nir Y. Attenuated Fast Steady-State Visual Evoked Potentials During Human Sleep Cereb Cortex 2018 28 1297 1311 10.1093/cercor/bhx043 28334175 · DOI
  39. [45]Bergmann T.O. Molle M. Schmidt M.A. Lindner C. Marshall L. Born J. Siebner H.R. EEG-Guided Transcranial Magnetic Stimulation Reveals Rapid Shifts in Motor Cortical Excitability during the Human Sleep Slow Oscillation J. Neurosci. 2012 32 243 253 10.1523/JNEUROSCI.4792-11.2012 22219286 PMC6621327 · DOI
  40. [46]Lustenberger C. Boyle M.R. Alagapan S. Mellin J.M. Vaughn B.V. Fröhlich F. Feedback-Controlled Transcranial Alternating Current Stimulation Reveals a Functional Role of Sleep Spindles in Motor Memory Consolidation Curr. Biol. 2016 26 2127 2136 10.1016/j.cub.2016.06.044 27476602 PMC4996680 · DOI
  41. [47]Paulus W. Transcranial electrical stimulation (tES—tDCS; tRNS, tACS) methods Neuropsychol. Rehabil. 2011 21 602 617 10.1080/09602011.2011.557292 21819181 · DOI
  42. [49]Mouze-Amady M. Sockeel P. Leconte P. Modification of REM sleep behavior by REMs contingent auditory stimulation in man Physiol. Behav. 1986 37 543 548 10.1016/0031-9384(86)90282-9 3749317 · DOI
  43. [50]Amzica F. Steriade M. The K-complex: Its slow (<1-Hz) rhythmicity and relation to delta waves Neurology 1997 49 952 959 10.1212/WNL.49.4.952 9339673 · DOI
  44. [51]Cash S.S. Halgren E. Dehghani N. Rossetti A.O. Thesen T. Wang C. Devinsky O. Kuzniecky R. Doyle W. Madsen J.R. The Human K-Complex Represents an Isolated Cortical Down-State Science 2009 324 1084 1087 10.1126/science.1169626 19461004 PMC3715654 · DOI · Paper page
  45. [52]Besedovsky L. Ngo H.V.V. Dimitrov S. Gassenmaier C. Lehmann R. Born J. Auditory closed-loop stimulation of EEG slow oscillations strengthens sleep and signs of its immune-supportive function Nat. Commun. 2017 8 10.1038/s41467-017-02170-3 29215045 PMC5719447 · DOI · Paper page
  46. [53]Robinson C. Bryant N. Maxwell J. Jones A. Robert B. Lamphere M. Combs A. Al Azzawi H. Gibson B. Sanguinetti J. The Benefits of Closed-Loop Transcranial Alternating Current Stimulation on Subjective Sleep Quality Brain Sci. 2018 8 204 10.3390/brainsci8120204 30469495 PMC6316321 · DOI
  47. [54]Ketz N. Jones A.P. Bryant N.B. Clark V.P. Pilly P.K. Closed-Loop Slow-Wave tACS Improves Sleep-Dependent Long-Term Memory Generalization by Modulating Endogenous Oscillations J. Neurosci. 2018 38 7314 7326 10.1523/JNEUROSCI.0273-18.2018 30037830 PMC6596034 · DOI
  48. [55]Steriade M. Corticothalamic resonance, states of vigilance and mentation Neuroscience 2000 101 243 276 10.1016/S0306-4522(00)00353-5 11074149 · DOI
  49. [56]Luthi A. McCormick D.A. Periodicity of thalamic synchronized oscillations: The role of Ca2+-mediated upregulation of Ih Neuron 1998 20 553 563 10.1016/S0896-6273(00)80994-0 9539128 · DOI
  50. [57]Timofeev I. Grenier F. Steriade M. Disfacilitation and active inhibition in the neocortex during the natural sleep-wake cycle: An intracellular study Proc. Natl. Acad. Sci. USA 2001 98 1924 1929 10.1073/pnas.98.4.1924 11172052 PMC29358 · DOI
  51. [58]Diekelmann S. Born J. SLEEP The memory function of sleep Nat. Rev. Neurosci. 2010 11 114 126 10.1038/nrn2762 20046194 · DOI · Paper page
  52. [59]Inostroza M. Born J. Sleep for Preserving and Transforming Episodic Memory Annu. Rev. Neurosci. 2013 36 79 102 10.1146/annurev-neuro-062012-170429 23642099 · DOI
  53. [60]Staresina B.P. Bergmann T.O. Bonnefond M. van der Meij R. Jensen O. Deuker L. Elger C.E. Axmacher N. Fell J. Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep Nat. Neurosci. 2015 18 1679 1686 10.1038/nn.4119 26389842 PMC4625581 · DOI · Paper page
  54. [61]Werk C.M. Harbour V.L. Chapman C.A. Induction of long-term potentiation leads to increased reliability of evoked neocortical spindles in vivo Neuroscience 2005 131 793 800 10.1016/j.neuroscience.2004.12.020 15749334 · DOI · Paper page
  55. [64]Choi J. Won K. Jun S.C. Acoustic Stimulation Following Sleep Spindle Activity May Enhance Procedural Memory Consolidation During a Nap IEEE Access. 2019 7 56297 56307 10.1109/ACCESS.2019.2913457 · DOI
  56. [65]Antony J.W. Piloto L. Wang M. Pacheco P. Norman K.A. Paller K.A. Sleep Spindle Refractoriness Segregates Periods of Memory Reactivation Curr. Biol. 2018 28 1736 1743.e4 10.1016/j.cub.2018.04.020 29804809 PMC5992601 · DOI · Paper page
  57. [66]Ngo H.-V.V. Seibold M. Boche D.C. Mölle M. Born J. Insights on auditory closed-loop stimulation targeting sleep spindles in slow oscillation up-states J. Neurosci. Methods 2019 316 117 124 10.1016/j.jneumeth.2018.09.006 30194953 · DOI
  58. [67]Fattinger S. Heinzle B.B. Ramantani G. Abela L. Schmitt B. Huber R. Closed-Loop Acoustic Stimulation During Sleep in Children With Epilepsy: A Hypothesis-Driven Novel Approach to Interact With Spike-Wave Activity and Pilot Data Assessing Feasibility Front. Hum. Neurosci. 2019 13 10.3389/fnhum.2019.00166 PMC6536690 31164813 · DOI
  59. [68]De Gennaro L. Ferrara M. Sleep spindles: An overview Sleep Med. Rev. 2003 7 423 440 10.1053/smrv.2002.0252 14573378 · DOI
  60. [69]Steriade M. Nunez A. Amzica F. A novel slow (<1 Hz) oscillation of neocortical neurons in vivo: Depolarizing and hyperpolarizing components J. Neurosci. 1993 13 3252 3265 10.1523/JNEUROSCI.13-08-03252.1993 8340806 PMC6576541 · DOI · Paper page
  61. [70]Achermann P. Borbely A.A. Low-frequency (<1 Hz) oscillations in the human sleep electroencephalogram Neuroscience 1997 81 213 222 10.1016/S0306-4522(97)00186-3 9300413 · DOI
  62. [71]Molle M. Marshall L. Gais S. Born J. Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep J. Neurosci. 2002 22 10941 10947 10.1523/JNEUROSCI.22-24-10941.2002 12486189 PMC6758415 · DOI · Paper page
  63. [72]Henin S. Borges H. Shankar A. Sarac C. Melloni L. Friedman D. Flinker A. Parra L.C. Buzsaki G. Devinsky O. Closed-Loop Acoustic Stimulation Enhances Sleep Oscillations But Not Memory Performance Eneuro 2019 6 ENEURO.0306-0319 10.1523/ENEURO.0306-19.2019 31604814 PMC6831893 · DOI
  64. [73]Bueno-Lopez A. Eggert T. Dorn H. Danker-Hopfe H. Slow oscillatory transcranial direct current stimulation (so-tDCS) during slow wave sleep has no effects on declarative memory in healthy young subjects Brain Stimul. 2019 12 948 958 10.1016/j.brs.2019.02.012 30842037 · DOI
  65. [74]Pilly P.K. Skorheim S.W. Hubbard R.J. Ketz N.A. Roach S.M. Lerner I. Jones A.P. Robert B. Bryant N.B. Hartholt A. One-Shot Tagging During Wake and Cueing During Sleep With Spatiotemporal Patterns of Transcranial Electrical Stimulation Can Boost Long-Term Metamemory of Individual Episodes in Humans Front. Neurosci. 2019 13 1416 10.3389/fnins.2019.01416 31998067 PMC6967741 · DOI
  66. [75]Ong J.L. Patanaik A. Chee N.I.Y.N. Lee X.K. Poh J.H. Chee M.W.L. Auditory stimulation of sleep slow oscillations modulates subsequent memory encoding through altered hippocampal function Sleep 2018 41 10.1093/sleep/zsy031 PMC5946855 29425369 · DOI
  67. [76]Bach Justesen A. Eskelund Johansen A.B. Martinussen N.I. Wasserman D. Terney D. Meritam P. Gardella E. Beniczky S. Added clinical value of the inferior temporal EEG electrode chain Clin. Neurophysiol. 2018 129 291 295 10.1016/j.clinph.2017.09.113 29102232 · DOI
  68. [77]Debellemaniere E. Chambon S. Pinaud C. Thorey V. Dehaene D. Léger D. Chennaoui M. Arnal P.J. Galtier M.N. Performance of an ambulatory dry-EEG device for auditory closed-loop stimulation of sleep slow oscillations in the home environment Front. Hum. Neurosci. 2018 12 10.3389/fnhum.2018.00088 PMC5853451 29568267 · DOI
  69. [78]Mikkelsen K.B. Tabar Y.R. Kappel S.L. Christensen C.B. Toft H.O. Hemmsen M.C. Rank M.L. Otto M. Kidmose P. Accurate whole-night sleep monitoring with dry-contact ear-EEG Sci. Rep. UK 2019 9 10.1038/s41598-019-53115-3 PMC6856384 31727953 · DOI
  70. [79]Ferster M.L. Lustenberger C. Karlen W. Configurable Mobile System for Autonomous High-Quality Sleep Monitoring and Closed-Loop Acoustic Stimulation IEEE Sens. Lett. 2019 3 10.1109/LSENS.2019.2914425 · DOI
  71. [80]Maskeliunas R. Damasevicius R. Martisius I. Vasiljevas M. Consumer-grade EEG devices: Are they usable for control tasks? PeerJ 2016 4 e1746 10.7717/peerj.1746 27014511 PMC4806709 · DOI
  72. [81]Ortega F.A. Butera R.J. Christini D.J. White J.A. Dorval A.D. Dynamic Clamp in Cardiac and Neuronal Systems Using RTXI Springer New York, NY, USA 2014 327 354 10.1007/978-1-4939-1096-0_21 PMC4880480 25023319 · DOI
  73. [82]Wu H. Ghekiere H. Beeckmans D. Tambuyzer T. Van Kuyck K. Aerts J.-M. Nuttin B. Conceptualization and validation of an open-source closed-loop deep brain stimulation system in rat Sci. Rep. 2015 4 9921 10.1038/srep09921 25897892 PMC4404680 · DOI
  74. [83]Gharabaghi A. Kraus D. Leã£O M.T. Spã¼Ler M. Walter A. Bogdan M. Rosenstiel W. Naros G. Ziemann U. Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: Enhancing motor cortex excitability for neurorehabilitation Front. Hum. Neurosci. 2014 8 10.3389/fnhum.2014.00122 24634650 PMC3942791 · DOI
  75. [84]Walter A. Murguialday A.R. Rosenstiel W. Birbaumer N. Bogdan M. Coupling BCI and cortical stimulation for brain-state-dependent stimulation: Methods for spectral estimation in the presence of stimulation after-effects Front. Neural Circuits 2012 6 10.3389/fncir.2012.00087 23162436 PMC3499764 · DOI
  76. [85]Zhao J. Li W. Mao X. Li M. SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots J. Vis. Exp. 2015 10.3791/53558 PMC4692746 26650051 · DOI
  77. [86]Mattout J. Perrin M. Bertrand O. Maby E. Improving BCI performance through co-adaptation: Applications to the P300-speller Ann. Phys. Rehabil. Med. 2015 58 23 28 10.1016/j.rehab.2014.10.006 25623293 · DOI
  78. [87]Broccard F.D. Mullen T. Chi Y.M. Peterson D. Iversen J.R. Arnold M. Kreutz-Delgado K. Jung T.-P. Makeig S. Poizner H. Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders Ann. Biomed. Eng. 2014 42 1573 1593 10.1007/s10439-014-1032-6 24833254 PMC4099421 · DOI
  79. [88]Mishra J. Gazzaley A. Closed-Loop Rehabilitation of Age-Related Cognitive Disorders Semin. Neurol. 2014 34 584 590 10.1055/s-0034-1396011 25520029 PMC4365941 · DOI
  80. [89]Laxpati N.G. Mahmoudi B. Gutekunst C.-A. Newman J.P. Zeller-Townson R. Gross R.E. Real-time in vivo optogenetic neuromodulation and multielectrode electrophysiologic recording with NeuroRighter Front. Neuroeng. 2014 7 10.3389/fneng.2014.00040 PMC4217045 25404915 · DOI
  81. [90]Ciliberti D. Michon F. Kloosterman F. Real-time classification of experience-related ensemble spiking patterns for closed-loop applications Elife 2018 7 10.7554/eLife.36275 PMC6207426 30373716 · DOI
  82. [91]Zrenner C. Belardinelli P. Muller-Dahlhaus F. Ziemann U. Closed-Loop Neuroscience and Non-Invasive Brain Stimulation: A Tale of Two Loops Front. Cell. Neurosci. 2016 10 10.3389/fncel.2016.00092 PMC4823269 27092055 · DOI
  83. [92]Hjorth B. EEG analysis based on time domain properties Electroencephalogr Clin. Neurophysiol. 1970 29 306 310 10.1016/0013-4694(70)90143-4 4195653 · DOI · Paper page
  84. [93]Sutton S. Braren M. Zubin J. John E.R. Evoked-potential correlates of stimulus uncertainty Science 1965 150 1187 1188 10.1126/science.150.3700.1187 5852977 · DOI · Paper page
  85. [95]Varona P. Arroyo D. Rodríguez F.B. Nowotny T. Chapter 6—Online Event Detection Requirements in Closed-Loop Neuroscience Closed Loop Neuroscience El Hady A. Academic Press San Diego, CA, USA 2016 81 91 Available online: 10.1016/B978-0-12-802452-2.00006-8 (accessed on 12 May 2020) · DOI
  86. [96]Buzsaki G. Rhythms of the Brain Oxford University Press Oxford, UK 2006
  87. [97]Muller K.R. Tangermann M. Dornhege G. Krauledat M. Curio G. Blankertz B. Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring J. Neurosci. Methods 2008 167 82 90 10.1016/j.jneumeth.2007.09.022 18031824 · DOI
  88. [98]Oostenveld R. Fries P. Maris E. Schoffelen J.M. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data Comput. Intel. Neurosci. 2011 10.1155/2011/156869 PMC3021840 21253357 · DOI · Paper page
  89. [99]Delorme A. Makeig S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis J. Neurosci. Methods 2004 134 9 21 10.1016/j.jneumeth.2003.10.009 15102499 · DOI · Paper page
  90. [100]Cox R. Schapiro A.C. Manoach D.S. Stickgold R. Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles Front. Hum. Neurosci. 2017 11 10.3389/fnhum.2017.00433 PMC5591792 28928647 · DOI
  91. [101]Vidal J.J. Toward direct brain-computer communication Annu. Rev. Biophys. Bioeng. 1973 2 157 180 10.1146/annurev.bb.02.060173.001105 4583653 · DOI
  92. [102]Kübler A. The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome Neuroethics 2019 10.1007/s12152-019-09409-4 · DOI
  93. [103]Jafarifarmand A. Badamchizadeh M.A. EEG Artifacts Handling in a Real Practical Brain-Computer Interface Controlled Vehicle IEEE Trans. Neural Syst. Rehabil. 2019 27 1200 1208 10.1109/TNSRE.2019.2915801 31095487 · DOI
  94. [104]Kreilinger A. Hiebel H. Muller-Putz G.R. Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback IEEE Trans. Biomed. Eng. 2016 63 519 529 10.1109/TBME.2015.2465866 26259213 · DOI
  95. [105]Ruffini G. Fox M.D. Ripolles O. Miranda P.C. Pascual-Leone A. Optimization of multifocal transcranial current stimulation for weighted cortical pattern targeting from realistic modeling of electric fields Neuroimage 2014 89 216 225 10.1016/j.neuroimage.2013.12.002 24345389 PMC3944133 · DOI
  96. [106]Cuypers K. Thijs H. Meesen R.L.J. Optimization of the Transcranial Magnetic Stimulation Protocol by Defining a Reliable Estimate for Corticospinal Excitability PLoS ONE 2014 9 10.1371/journal.pone.0086380 24475111 PMC3901672 · DOI
  97. [107]Hariz M.I. Rehncrona S. Quinn N.P. Speelman J.D. Wensing C. Multicenter study on deep brain stimulation in Parkinson’s disease: An independent assessment of reported adverse events at 4 years Mov. Disord. 2008 23 416 421 10.1002/mds.21888 18067188 · DOI
  98. [108]Hebb A.O. Ojemann G.A. The thalamus and language revisited Brain Lang 2013 126 99 108 10.1016/j.bandl.2012.06.010 22857902 · DOI
  99. [109]Tsunematsu T. Kilduff T.S. Boyden E.S. Takahashi S. Tominaga M. Yamanaka A. Acute Optogenetic Silencing of Orexin/Hypocretin Neurons Induces Slow-Wave Sleep in Mice J. Neurosci. 2011 31 10529 10539 10.1523/JNEUROSCI.0784-11.2011 21775598 PMC3864636 · DOI
  100. [110]Ladenbauer J. Ladenbauer J. Kulzow N. de Boor R. Avramova E. Grittner U. Floel A. Promoting Sleep Oscillations and Their Functional Coupling by Transcranial Stimulation Enhances Memory Consolidation in Mild Cognitive Impairment J. Neurosci. 2017 37 7111 7124 10.1523/JNEUROSCI.0260-17.2017 28637840 PMC6705731 · DOI
  101. [111]Carskadon M.A. Dement W.C. Monitoring and Staging Human Sleep Principles and Practice of Sleep Medicine 5th ed. Kryger M.H. Roth T. Dement W.C. Elsevier Saunders St. Louis, MO, USA 2011
  102. [112]Michielli N. Acharya U.R. Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals Comput. Biol. Med. 2019 106 71 81 10.1016/j.compbiomed.2019.01.013 30685634 · DOI
  103. [113]Lee H. Choi J. Kim S. Jun S.C. Lee B.G. A Compressive Sensing-Based Automatic Sleep-Stage Classification System With Radial Basis Function Neural Network IEEE Access. 2019 7 186499 186509 10.1109/ACCESS.2019.2961326 · DOI
  104. [114]Patanaik A. Ong J.L. Gooley J.J. Ancoli-Israel S. Chee M.W.L. An end-to-end framework for real-time automatic sleep stage classification Sleep 2018 41 10.1093/sleep/zsy041 PMC5946920 29590492 · DOI
  105. [115]Phan H. Andreotti F. Cooray N. Chen O.Y. De Vos M. Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification IEEE Trans. Biomed. Eng. 2019 66 1285 1296 10.1109/TBME.2018.2872652 30346277 PMC6487915 · DOI
  106. [116]Taran S. Sharma P.C. Bajaj V. Automatic sleep stages classification using optimize flexible analytic wavelet transform Knowl-Based Syst. 2020 192 10.1016/j.knosys.2019.105367 · DOI
  107. [117]Kulkarni P.M. Xiao Z. Robinson E.J. Jami A.S. Zhang J. Zhou H. Henin S.E. Liu A.A. Osorio R.S. Wang J. A deep learning approach for real-time detection of sleep spindles J. Neural Eng. 2019 16 036004 10.1088/1741-2552/ab0933 30790769 PMC6527330 · DOI
  108. [118]Smith M.E. McEvoy L.K. Gevins A. Neurophysiological indices of strategy development and skill acquisition Cogn. Brain Res. 1999 7 389 404 10.1016/S0926-6410(98)00043-3 9838200 · DOI
  109. [119]McEvoy L.K. Pellouchoud E. Smith M.E. Gevins A. Neurophysiological signals of working memory in normal aging Cogn. Brain Res. 2001 11 363 376 10.1016/S0926-6410(01)00009-X 11339986 · DOI
  110. [121]Buysse D.J. Reynolds C.F. 3rd Monk T.H. Berman S.R. Kupfer D.J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research Psychiatry Res. 1989 28 193 213 10.1016/0165-1781(89)90047-4 2748771 · DOI · Paper page

Cited by

Sorted by citations

Authors
Choi, J., Kwon, M., & Jun, S. C.
Affiliations
Gwangju Institute of Science and Technology (KR)
Corresponding author
Jun, Sung Chan
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2020-05-13
Volume
20
Issue
10
Pages
2770
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s20102770
PMC
PMC7285770
OpenAlex
W3024577541
Cited by
35
References
9
Citations by year
2026: 2 · 2025: 12 · 2024: 3 · 2023: 7 · 2022: 8 · 2021: 3
FWCI
0.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation of Korea (No. 2016R1A2B4010897)
  • Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP) (No. 2017-0-00451)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
Keywords
closed-loop feedback, sleep, neurofeedback, brain stimulation, open-source library, memory consolidation, sleep research
MeSH terms
Brain, Feedback, Humans, Sleep, Polysomnography

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review