9,195
Views
21
CrossRef citations to date
0
Altmetric
Articles

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

ORCID Icon, , , , , , , , ORCID Icon, , , , ORCID Icon, ORCID Icon, , , , , , , , , , ORCID Icon, ORCID Icon, , , , ORCID Icon & ORCID Icon show all
Pages 3-36 | Received 30 Jul 2016, Accepted 19 Dec 2016, Published online: 30 Jan 2017

References

  • Wolpaw JR, Birbaumer N, Heetderks WJ, et al. Brain–computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng. 2000 Jun;8(2):164–173.10.1109/TRE.2000.847807
  • Vaughan TM, Heetderks WJ, Trejo LJ, et al. Brain–computer interface technology: a review of the second international meeting. IEEE Trans Neural Syst Rehabil Eng. 2003 Jun;11(2):94–109.10.1109/TNSRE.2003.814799
  • Vaughan TM, Wolpaw JR. The third international meeting on Brain–computer interface technology: making a difference. IEEE Trans Neural Syst Rehabil Eng. 2006 Jun;14(2):126–127.
  • Vaughan TM, Wolpaw JR. Special issue containing contributions from the fourth international Brain–computer interface meeting. J Neural Eng. 2011 Apr;8(2):1–2. doi:10.1088/1741-2560/8/2/020201. Epub 2011 Mar 24.
  • Huggins J, Guger C, Allison B, et al. Workshops of the fifth international Brain–computer interface meeting: defining the future. Brain Comput Interfaces J. 2014;1(1):27–49.10.1080/2326263X.2013.876724
  • Huggins JE, Wolpaw JR. Papers from the fifth international Brain–computer interface meeting. preface. J Neural Eng. 2014 Jun;11(3): 030301. doi:10.1088/1741-2560/11/3/030301. Epub 2014 May 19.
  • Daly JJ, Huggins JE. Brain–computer interface: current and emerging rehabilitation applications. Arch Phys Med Rehabil. 2015 Mar;96(3):S1–S7.10.1016/j.apmr.2015.01.007
  • Giacino JT, Kalmar K, Whyte J. The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004 Dec;85(12):2020–2029.10.1016/j.apmr.2004.02.033
  • Coyle D, Stow J, McCreadie K, et al. Sensorimotor modulation assessment and Brain–computer interface training in disorders of consciousness. Arch Phys Med Rehabil. 2015 Mar;96(3):S62–S70.10.1016/j.apmr.2014.08.024
  • Lugo ZR, Rodriguez J, Lechner A, et al. A vibrotactile p300-based Brain–computer interface for consciousness detection and communication. Clin EEG Neurosci. 2014 Jan;45(1):14–21.10.1177/1550059413505533
  • Guger C, Kapeller C, Ortner R, et al. Motor imagery with Brain–computer interface neurotechnology. In: Garcia BM, editor. Motor imagery: emerging practices, role in physical therapy and clinical implications; 2015. p. 61–79.
  • Tzovara A, Rossetti AO, Juan E, et al. Prediction of awakening from hypothermic post anoxic coma based on auditory discrimination. Ann Neurol. 2016 Feb; 79(5):748–757.
  • Schettini F, Risetti M, Arico P, et al. P300 latency jitter occurrence in patients with disorders of consciousness: toward a better design for brain computer interface applications. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:6178–6181.
  • Morone G, Pisotta I, Pichiorri F, et al. Proof of principle of a Brain–computer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability. Arch Phys Med Rehabil. 2015 Mar;96(3):S71–S78.10.1016/j.apmr.2014.05.026
  • Cincotti F, Pichiorri F, Arico P, et al. EEG-based Brain–computer interface to support post-stroke motor rehabilitation of the upper limb. Conf Proc IEEE Eng Med Biol Soc; 2012; 2012. p. 4112–4115.
  • Pichiorri F, Morone G, Petti M, et al. Brain–computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015 May;77(5):851–865.10.1002/ana.24390
  • Mrachacz-Kersting N, Kristensen SR, Niazi IK, et al. Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity. J Physiol. 2012 Apr 1;590(7):1669–1682.10.1113/jphysiol.2011.222851
  • Mrachacz-Kersting N, Jiang N, Stevenson AJ, et al. Efficient neuroplasticity induction in chronic stroke patients by an associative Brain–computer interface. J Neurophysiol. 2016 Mar;115(3):1410–1421.10.1152/jn.00918.2015
  • Toppi J, Mattia D, Anzolin A, et al. Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:6786–6789.
  • Soekadar SR, Witkowski M, Birbaumer N, et al. Enhancing hebbian learning to control brain oscillatory activity. Cereb Cortex. 2015 Sep;25(9):2409–2415.10.1093/cercor/bhu043
  • Biasiucci A, Leeb R, Al-Khodairy A, et al. Motor recovery after stroke by means of BCI-guided functional electrical stimulation. 5th International Brain–computer Interface Meeting; 2013; Pacific Grove, California; 2013. p. 1–2.
  • Remsik A, Young B, Vermilyea R, et al. A review of the progression and future implications of Brain–computer interface therapies for restoration of distal upper extremity motor function after stroke. Expert Rev Med Devices. 2016 May;13(5):445–454.10.1080/17434440.2016.1174572
  • Ono T, Shindo K, Kawashima K, et al. Brain–computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front Neuroeng. 2014 Jul 7;7:19.
  • Daly JJ, Sitaram R. BCI therapeutic applications for improving brain function. In: Wolpaw JR, Wolpaw EW, editors. Brain–computer interfaces: principles and practice. New York (NY): Oxford University Press; 2012. p. 351–362.
  • Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: A systematic review. Lancet Neurol. 2009 Aug;8(8):741–754.10.1016/S1474-4422(09)70150-4
  • Marino RJ, Ditunno JF Jr, Donovan WH, et al. Neurologic recovery after traumatic spinal cord injury: data from the model spinal cord injury systems. Arch Phys Med Rehabil. 1999 Nov;80(11):1391–1396.10.1016/S0003-9993(99)90249-6
  • Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet. 2012 Dec;381(9866):557–564.
  • Hochberg LR, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012 May 16;485(7398):372–375.10.1038/nature11076
  • Aflalo T, Kellis S, Klaes C, et al. Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science. 2015 May 22;348(6237):906–910.10.1126/science.aaa5417
  • Do AH, Wang PT, King CE, et al. Brain–computer interface controlled robotic gait orthosis. J Neuroeng Rehabil. 2013 Dec 9;10:111. doi:10.1186/1743-0003-10-111.
  • King CE, Wang PT, McCrimmon CM, et al. The feasibility of a Brain–computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia. J Neuroeng Rehabil. 2015 Sep 24;12:470.10.1186/s12984-015-0068-7
  • Soekadar SR, Birbaumer N, Slutzky MW, et al. Brain–machine interfaces in neurorehabilitation of stroke. Neurobiol Dis. 2015 Nov;83:172–179.10.1016/j.nbd.2014.11.025
  • McCrimmon CM, King CE, Wang PT, et al. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J Neuroeng Rehabil 2015 Jul 11;12:e2.10.1186/s12984-015-0050-4
  • Rushton DN. Functional electrical stimulation and rehabilitation–an hypothesis. Med Eng Phys. 2003 Jan;25(1):75–78.10.1016/S1350-4533(02)00040-1
  • Ramos-Murguialday A, Broetz D, Rea M, et al. Brain–machine interface in chronic stroke rehabilitation: A controlled study. Ann Neurol. 2013 Jul;74(1):100–108.10.1002/ana.v74.1
  • Simeral JD, Kim SP, Black MJ, et al. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J Neural Eng. 2011 Apr;8(2): 025027.10.1088/1741-2560/8/2/025027
  • Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006 Jul 13;442(7099):164–171.
  • Wodlinger B, Downey JE, Tyler-Kabara EC, et al. Ten-dimensional anthropomorphic arm control in a human brain–machine interface: difficulties, solutions, and limitations. J Neural Eng. 2015 Feb;12(1): 016011. doi:10.1088/1741-2560/12/1/016011. Epub 2014 Dec 16.
  • Andersen RA, Buneo CA. Intentional maps in posterior parietal cortex. Annu Rev Neurosci. 2002;25:189–220.10.1146/annurev.neuro.25.112701.142922
  • Mulliken GH, Musallam S, Andersen RA. Decoding trajectories from posterior parietal cortex ensembles. J Neurosci. 2008 Nov 26;28(48):12913–12926.10.1523/JNEUROSCI.1463-08.2008
  • Aflalo T, Kellis S, Klaes C, et al. Neurophysiology. decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science. 2015 May 22;348(6237):906–910.10.1126/science.aaa5417
  • Klaes C, Kellis S, Aflalo T, et al. Hand shape representations in the human posterior parietal cortex. J Neurosci. 2015 Nov 18;35(46):15466–15476.10.1523/JNEUROSCI.2747-15.2015
  • Collinger JL, Boninger ML, Bruns TM, et al. Functional priorities, assistive technology, and Brain–computer interfaces after spinal cord injury. J Rehabil Res Dev. 2013 Apr;50(2):145–160.10.1682/JRRD.2011.11.0213
  • Chadwick EK, Blana D, Simeral JD, et al. Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia. J Neural Eng. 2011 Jun;8(3): 034003. Epub 2011 May 5.10.1088/1741-2560/8/3/034003
  • Ajiboye AB, Simeral JD, Donoghue JP, et al. Prediction of imagined single-joint movements in a person with high-level tetraplegia. IEEE Trans Biomed Eng. 2012 Oct;59(10):2755–2765.10.1109/TBME.2012.2209882
  • Kilgore KL, Hoyen HA, Bryden AM, et al. An implanted upper-extremity neuroprosthesis using myoelectric control. J Hand Surg. 2008;33(4):539–550.10.1016/j.jhsa.2008.01.007
  • Peckham PH, Keith MW, Kilgore KL, et al. Implantable neuroprosthesis research group. efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: a multicenter study. Arch Phys Med Rehabil. 2001 Oct;82(10):1380–1388.10.1053/apmr.2001.25910
  • Yanagisawa T, Hirata M, Saitoh Y, et al. Real-time control of a prosthetic hand using human electrocorticography signals. J Neurosurg. 2011 Jun;114(6):1715–1722.10.3171/2011.1.JNS101421
  • Yanagisawa T, Hirata M, Saitoh Y, et al. Electrocorticographic control of a prosthetic arm in paralyzed patients. Ann Neurol. 2012 Mar;71(3):353–361.10.1002/ana.22613
  • Wang W, Collinger JL, Degenhart AD, et al. An electrocorticographic brain interface in an individual with tetraplegia. PLoS One. 2013;8(2):e55344.10.1371/journal.pone.0055344
  • Chao ZC, Nagasaka Y, Fujii N. Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front Neuroeng. 2010;3:3.
  • Flesher S, Collinger JL, Foldes ST, et al. Intracortical Microstimulation as a Feedback Source for Brain–computer Interface Users. Proceedings of the 6th International Brain–computer Interface Meeting; 2016; Pacific Grove, California; 2016. p. 138.
  • Salas MA, Kramer DR, Lee B, et al. Artificial sensations through cortical stimulation of human somatosensory cortex with subdural mini-ECoG grids. In: Society for Neuroscience Abstracts; 2016 Nov 12–16; San Diego, CA; 2016. p. Abstract 248.03/TT14.
  • Katyal KD, Johannes MS, Kellis S, et al. A collaborative BCI approach to autonomous control of a prosthetic limb system. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2014; San Diego; 2014. p. 1479–1482.
  • Downey JE, Weiss JM, Muelling K, et al. Blending of brain–machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. J Neuroeng Rehabil. 2016 Mar 18;13:372.10.1186/s12984-016-0134-9
  • Rupp R, Müller-Putz G BCI-Controlled Grasp Neuroprosthesis in High Spinal Cord Injury. Converging Clin Eng Res Neurorehabilitation; 2012;1:1255–1259.
  • Rupp R, Kreilinger A, Rohm M, et al. Development of a non-invasive, multifunctional grasp neuroprosthesis and its evaluation in an individual with a high spinal cord injury. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:1835–1838.
  • Muller-Putz GR, Breitwieser C, Cincotti F, et al. Tools for Brain–computer interaction: a general concept for a hybrid BCI. Front Neuroinform. 2011;5:30.
  • Müller-Putz G, Leeb R, Tangermann M, et al. Towards non-invasive hybrid Brain–computer interfaces: framework, practice, clinical application and beyond. Proc IEEE. 2015;103(6):926–943.10.1109/JPROC.2015.2411333
  • Kreilinger A, Kaiser V, Breitwieser C, et al. Switching between manual control and Brain–computer interface using long term and short term quality measures. Front Neurosci. 2011;5:147.
  • Muller-Putz GR, Scherer R, Pfurtscheller G, et al. EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci Lett. 2005 Jul 1-8;382(1-2):169–174.10.1016/j.neulet.2005.03.021
  • Pfurtscheller G, Müller GR, Pfurtscheller J, et al. ‘Thought’–control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett. 2003 Nov 6;351(1):33–36.10.1016/S0304-3940(03)00947-9
  • Rohm M, Schneiders M, Müller C, et al. Hybrid Brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artif Intell Med. 2013 Oct;59(2):133–142.10.1016/j.artmed.2013.07.004
  • Muller-Putz GR, Scherer R, Pfurtscheller G, et al. Brain–computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation. Biomed Tech (Berl). 2006 Jul;51(2):57–63.10.1515/BMT.2006.011
  • Rupp R, Rohm M, Schneiders M, et al. Functional rehabilitation of the paralyzed upper extremity after spinal cord injury by noninvasive hybrid neuroprosthesis. Proc IEEE. 2015;103(6):954–968.10.1109/JPROC.2015.2395253
  • Kreilinger A, Kaiser V, Rohm M, et al. Neuroprosthesis Control via Non-invasive Hybrid Brain–computer Interface,. IEEE intelligent systems. 2013;2840–43.
  • Rohm M, Rupp R, Schneiders M, et al. Hybrid Brain–computer interfaces for control of neuroprosthetic systems for restoration of upper limb functions in high spinal cord injured individuals. Proceedings IFESS 2013. 2013
  • Pereira J, Ofner P, Muller-Putz GR. Goal-directed or aimless? EEG differences during the preparation of a reach-and-touch task. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:1488–1491.
  • Ofner P, Muller-Putz GR. Using a noninvasive decoding method to classify rhythmic movement imaginations of the arm in two planes. IEEE Trans Biomed Eng. 2015 Mar;62(3):972–981.10.1109/TBME.2014.2377023
  • Ofner P, Muller-Putz GR. Decoding of velocities and positions of 3D arm movement from EEG. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:6406–6409.
  • Schwarz A, Schwarz A, Ofner P, et al. Time domain classification of grasp and hold tasks. Proceedings of the 6th International Brain–computer Interface Meeting; 2016; Pacific Grove, California; 2016. p. 76.
  • Bamdad M, Zarshenas H, Auais MA. Application of BCI systems in neurorehabilitation: a scoping review. Disabil Rehabil Assist Technol. 2015;10(5):355–364.10.3109/17483107.2014.961569
  • Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol. 2008 Nov;7(11):1032–1043.10.1016/S1474-4422(08)70223-0
  • Salisbury DB, Dahdah M, Driver S, et al. Virtual reality and brain computer interface in neurorehabilitation. Proc (Bayl Univ Med Cent). 2016 Apr;29(2):124–127.
  • Cameirao MS, Badia SB, Oller ED, et al. Neurorehabilitation using the virtual reality based rehabilitation gaming system: methodology, design, psychometrics, usability and validation. J Neuroeng Rehabil. 2010 Sep 22;7:48.10.1186/1743-0003-7-48
  • Sun H, Xiang Y, Yang M. Neurological rehabilitation of stroke patients via motor imaginary-based brain–computer interface technology. Neural Regen Res. 2011;6(28):2198–2202.
  • Treder M, Blankertz B. (C)overt attention and visual speller design in an ERP-based Brain–computer interface. Behav Brain Funct. 2010;6(1):28.10.1186/1744-9081-6-28
  • Hill NJ, Ricci E, Haider S, et al. A practical, intuitive Brain–computer interface for communicating ‘yes’ or ‘no’ by listening. J Neural Eng. 2014 Jun;11(3): 035003. Epub 2014 May 19.10.1088/1741-2560/11/3/035003
  • Lim CG, Lee TS, Guan C, et al. A Brain–computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS One. 2012;7(10): e46692.10.1371/journal.pone.0046692
  • Ehlers J, Valbuena D, Stiller A, et al. Age-specific mechanisms in an SSVEP-based BCI scenario: evidences from spontaneous rhythms and neuronal oscillators. Comput Intell Neurosci. 2012;2012:967305.
  • Ding XQ, Sun Y, Braass H, et al. Evidence of rapid ongoing brain development beyond 2 years of age detected by fiber tracking. AJNR Am J Neuroradiol. 2008 Aug;29(7):1261–1265.10.3174/ajnr.A1097
  • Giedd JN, Blumenthal J, Jeffries NO, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999 Oct;2(10):861–863.10.1038/13158
  • Deng W. Neurobiology of injury to the developing brain. Nat Rev Neurol. 2010 Jun;6(6):328–336.10.1038/nrneurol.2010.53
  • Johnston MV. Plasticity in the developing brain: implications for rehabilitation. Dev Disabil Res Rev. 2009;15(2):94–101.10.1002/ddrr.v15:2
  • Pannek K, Boyd RN, Fiori S, et al. Assessment of the structural brain network reveals altered connectivity in children with unilateral cerebral palsy due to periventricular white matter lesions. Neuroimage Clin. 2014 Jun;5(5):84–92.10.1016/j.nicl.2014.05.018
  • Kothari R, Singh R, Singh S, et al. Neurophysiologic findings in children with spastic cerebral palsy. J Pediatr Neurosci. 2010 Jan;5(1):12–17.10.4103/1817-1745.66671
  • Townsend J, Westerfield M, Leaver E, et al. Event-related brain response abnormalities in autism: evidence for impaired cerebello-frontal spatial attention networks. Brain Res Cogn Brain Res. 2001 Mar;11(1):127–145.10.1016/S0926-6410(00)00072-0
  • Wang J, Barstein J, Ethridge LE, et al. Resting state EEG abnormalities in autism spectrum disorders. J Neurodev Disord. 2013 Sep 16;5(1):24.10.1186/1866-1955-5-24
  • Boutros NN, Lajiness-O’Neill R, Zillgitt A, et al. EEG changes associated with autistic spectrum disorders. Neuropsych Electrophys. 2015;1(3):1–20.
  • Geschwind DH, Levitt P. Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol. 2007 Feb;17(1):103–111.10.1016/j.conb.2007.01.009
  • Ousley O, Cermak T. Autism spectrum disorder: defining dimensions and subgroups. Curr Dev Disord Rep. 2014 Mar 1;1(1):20–28.10.1007/s40474-013-0003-1
  • Shevell MI. Classifying cerebral palsy subtypes. Future Neurol. 2010;5(5):765–775.10.2217/fnl.10.46
  • Makeig S, Bell AJ, Jung TP, et al. Independent component analysis of electroencephalographic data. Proceedings of the 1995 conference; 1996; Kaufmann; 1996. p. 145.
  • Makeig S, Debener S, Onton J, et al. Mining event-related brain dynamics. Trends Cogn Sci. 2004 May;8(5):204–210.10.1016/j.tics.2004.03.008
  • Evans AC. Brain Development Cooperative Group. The NIH MRI study of normal brain development. Neuroimage. 2006 Mar;30(1):184–202.10.1016/j.neuroimage.2005.09.068
  • Almli CR, Rivkin MJ, McKinstry RC. Brain Development Cooperative Group. the NIH MRI study of normal brain development (objective-2): newborns, infants, toddlers, and preschoolers. Neuroimage. 2007 Mar;35(1):308–325.10.1016/j.neuroimage.2006.08.058
  • Gavin WJ, Davies PL. Obtaining reliable psychophysiological data with child participants: methodological considerations. In: Schmidt LA, Segalowitz SJ, editors. Developmental psychophysiology: theory, systems, and methods. New York (NY): Cambridge University Press; 2008. p. 424–447.
  • Slater JD, Kalamangalam GP, Hope O. Quality assessment of electroencephalography obtained from a “dry electrode” system. J Neurosci Methods. 2012 Jul 15;208(2):134–137.10.1016/j.jneumeth.2012.05.011
  • Sellers E, Turner P, Sarnacki W, et al. A novel dry electrode for Brain–computer interface. In: Jacko J, editor. Human-Computer Interactions, Part II HCII 2009, LNCS 5611. Berlin / Heidelberg: Springer; 2009. p. 623–631.
  • David Hairston W, Whitaker KW, Ries AJ, et al. Usability of four commercially-oriented EEG systems. J Neural Eng. 2014 Aug;11(4): 046018. Epub 2014 Jul 1.10.1088/1741-2560/11/4/046018
  • Kohli S, Casson AJ. Towards out-of-the-lab EEG in uncontrolled environments: feasibility study of dry EEG recordings during exercise bike riding. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:1025–1028.
  • Hsu SH, Mullen T, Jung TP, et al. Online recursive independent component analysis for real-time source separation of high-density EEG. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:3845–3848.
  • Halder S, Bensch M, Mellinger J, et al. Online artifact removal for Brain–computer interfaces using support vector machines and blind source separation. Comput Intell Neurosci. 2007;2007:82069.
  • Kilicarslan A, Grossman RG, Contreras-Vidal JL A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements. J Neural Eng. 2016 Apr;13(2): 026013. Epub 2016 Feb 10.10.1088/1741-2560/13/2/026013
  • Suchman, LA Plans and situated actions: the problem of human-machine communication. Cambridge Cambridgeshire; New York: Cambridge University Press; 1987. p. 203.
  • Tufte, ER Envisioning information. Cheshire, Conn. P.O. Box 430, Cheshire 06410: Graphics Press; 1990. p. 126.
  • Zander TO, Kothe C. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J Neural Eng. 2011 Apr;8(2): 025005. Epub 2011 Mar 24.10.1088/1741-2560/8/2/025005
  • Zander TO, Jatzev S Context-aware brain–computer interfaces: exploring the information space of user, technical system and environment. J Neural Eng. 2012 Feb;9(1): 016003. Epub 2011 Dec 7.10.1088/1741-2560/9/1/016003
  • Zander TO, Brönstrup J, Lorenz R, et al. Towards BCI-based implicit control in human–computer interaction In: Fairclough SH, Gilleade K, editors. Advances in Physiological Computing. Berlin, Germany: Springer; 2014; p. 67–90.
  • Rosenboom D, editor. Biofeedback and the arts: results of early experiments. Vancouver: A.R.C. Publications; 1976.
  • Nijholt A, Nam CS. Editorial: arts and Brain–computer interfaces (BCIs). Brain Comput Interfaces. 2015;2(2–3):57–59.10.1080/2326263X.2015.1100514
  • Nijholt A. Competing and collaborating brains: multi-brain computer interfacing In: Hassanieu AE, Azar AT, editors. Brain–computer interfaces: current trends and applications. Intelligent Systems Reference Library series, Vol. 74. Berlin: Springer; 2015. p. 313–35.
  • Andujar M, Crawford CS, Nijholt A, et al. Defining artistic Brain–computer interfaces: expressing and stimulating the user’s affective state. Brain Comput Interfaces. 2015;2(2–3):60–69.10.1080/2326263X.2015.1104613
  • Gürkök H, Nijholt A. Affective Brain–computer interfaces for arts. In: Nijholt A, D’Mello S, Pantic M, editors. Proceedings 5th biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII 2013); 2013. p. 827–831.
  • Botrel L, Holz EM, Kübler A. Brain painting V2: evaluation of P300-based Brain–computer interface for creative expression by an end-user following the user-centered design. Brain Comput Interfaces. 2015;2(2-3):135–149.10.1080/2326263X.2015.1100038
  • Schiavone G, Großekathöfer U, à Campo S, et al. Towards real-time visualization of a juggler’s brain. Brain Comput Interfaces. 2015;2(2–3):90–102.10.1080/2326263X.2015.1101656
  • Brouwer AM, Hogervorst M, Reuderink B, et al. Physiological signals distinguish between reading emotional and non-emotional sections in a novel. Brain Comput Interfaces. 2015;2(2-3):76–89.10.1080/2326263X.2015.1100037
  • Mullen T, Khalil A, Ward T, et al. MindMusic: playful and social installations at the interface between music and the brain In: Nijholt A, editor. More playful user interfaces. Interfaces that invite social and physical interaction. Gaming Media and Social Effects series. Singapore: Springer; 2015; p. 197–229.
  • Wadeson A, Nijholt A, Nam CS. Artistic Brain–computer interfaces: current state-of-art of control mechanisms. Brain Comput Interfaces. 2015;2(2-3):70–75.10.1080/2326263X.2015.1103155
  • Golub MD, Chase SM, Batista AP, et al. Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr Opin Neurobiol. 2016 Apr;37:53–58.10.1016/j.conb.2015.12.005
  • Law AJ, Rivlis G, Schieber MH. Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons. J Neurophysiol. 2014 Sep 15;112(6):1528–1548.10.1152/jn.00373.2013
  • Rouse AG, Williams JJ, Wheeler JJ, et al. Cortical adaptation to a chronic micro-electrocorticographic brain computer interface. J Neurosci. 2013 Jan 23;33(4):1326–1330.10.1523/JNEUROSCI.0271-12.2013
  • Sadtler PT, Quick KM, Golub MD, et al. Neural constraints on learning. Nature. 2014 Aug 28;512(7515):423–426.10.1038/nature13665
  • Ganguly K, Carmena JM. Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol. 2009 Jul;7(7): e1000153.10.1371/journal.pbio.1000153
  • Ganguly K, Dimitrov DF, Wallis JD, et al. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat Neurosci. 2011 May;14(5):662–667.10.1038/nn.2797
  • Gulati T, Ramanathan DS, Wong CC, et al. Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning. Nat Neurosci. 2014 Aug;17(8):1107–1113.10.1038/nn.3759
  • Tonin L, Leeb R, Millán JR. Time-dependent approach for single trial classification of covert visuospatial attention. J Neural Eng. 2012 Aug;9(4): 045011. Epub 2012 Jul 25.10.1088/1741-2560/9/4/045011
  • Sajda P, Pohlmeyer E, Wang J, et al. In a blink of an eye and a switch of a transistor: cortically coupled computer vision, 98. Proc IEEE. 2010;98:462–478.10.1109/JPROC.2009.2038406
  • Kohlmorgen J, Dornhege G, Braun ML, et al. Improving human performance in a real operating environment through real-time mental workload detection. In: Dornhege G, Millán JdR, Hinterberger T, et al., editors. Toward Brain–computer interfacing. Cambridge, MA: MIT press; 2007. p. 409–422.
  • Dourish, P. Where the action is: the foundations of embodied interaction. Cambridge, MA: MIT press; 2004.
  • Carlson T, Millan JDR. Brain-controlled wheelchairs: a robotic architecture. IEEE Robot Autom Mag. 2013;20:65–73.10.1109/MRA.2012.2229936
  • Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol. 1988 Dec;70(6):510–523.10.1016/0013-4694(88)90149-6
  • Acqualagna L, Blankertz B. Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP). Clin Neurophysiol. 2013 May;124(5):901–908.10.1016/j.clinph.2012.12.050
  • Nicolae IE, Acqualagna L, Blankertz B. Tapping neural correlates of the depth of cognitive processing for improving human computer interaction. Symbiotic Interaction Volume 9359 of the series Lecture Notes in Computer Science: 4th International Workshop, Symbiotic 2015, Berlin, Germany, October 7–8, 2015, Proceedings. Switzerland: Springer International Publishing; 2015; p. 126–131. Available from: http://link.springer.com/chapter/10.1007%2F978-3-319-24917-9_13
  • Schalk G, Wolpaw JR, McFarland DJ, et al. EEG-based communication: presence of an error potential. Clin Neurophysiol. 2000 Dec;111(12):2138–2144.10.1016/S1388-2457(00)00457-0
  • Chavarriaga R, Millan JR. Learning from EEG error-related potentials in noninvasive Brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2010 Aug;18(4):381–388.10.1109/TNSRE.2010.2053387
  • Iturrate I, Montesano L, Minguez J. Task-dependent signal variations in EEG error-related potentials for Brain–computer interfaces. J Neural Eng. 2013 Apr;10(2): 026024. Epub 2013 Mar 26.10.1088/1741-2560/10/2/026024
  • Iturrate I, Chavarriaga R, Montesano L, et al. Latency correction of error potentials between different experiments reduces calibration time for single-trial classification. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:3288–3291.
  • Chavarriaga R, Sobolewski A, Millan JR. Errare machinale est: the use of error-related potentials in brain–machine interfaces. Front Neurosci. 2014 Jul;22(8):208.
  • Dal Seno B, Matteucci M, Mainardi L. Online detection of P300 and error potentials in a BCI speller. Comput Intell Neurosci. 2010;2010:307254.
  • Iturrate I, Chavarriaga R, Montesano L, et al. Teaching brain–machine interfaces as an alternative paradigm to neuroprosthetics control. Sci Rep. 2015 Sep;5(5):13893.10.1038/srep13893
  • Iturrate I, Montesano L, Minguez J. Shared-control Brain–computer interface for a two dimensional reaching task using EEG error-related potentials. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:5258–5262.
  • Lew E, Chavarriaga R, Silvoni S, et al. Detection of self-paced reaching movement intention from EEG signals. Front Neuroeng. 2012;5:13.
  • Bai O, Rathi V, Lin P, et al. Prediction of human voluntary movement before it occurs. Clin Neurophysiol. 2011 Feb;122(2):364–372.10.1016/j.clinph.2010.07.010
  • Khaliliardali Z, Chavarriaga R, Gheorghe LA, et al. Action prediction based on anticipatory brain potentials during simulated driving. J Neural Eng. 2015 Dec;12(6): 066006. Epub 2015 Sep 24.10.1088/1741-2560/12/6/066006
  • Andersen RA, Snyder LH, Bradley DC, et al. Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annu Rev Neurosci. 1997;20:303–330.10.1146/annurev.neuro.20.1.303
  • Miller KJ, Leuthardt EC, Schalk G, et al. Spectral changes in cortical surface potentials during motor movement. J Neurosci. 2007 Feb 28;27(9):2424–2432.10.1523/JNEUROSCI.3886-06.2007
  • Kubanek J, Brunner P, Gunduz A, et al. The tracking of speech envelope in the human cortex. PLoS One. 2013;8(1):e53398.10.1371/journal.pone.0053398
  • Lotte F, Brumberg JS, Brunner P, et al. Electrocorticographic representations of segmental features in continuous speech. Front Hum Neurosci. 2015 Feb;24(9):97.
  • Mugler E, Goldrick M, Tate M, et al. Articulatory gestures are insensitive to within-word context. Proceedings of the 6th International Brain–computer Interface Meeting; 2016; Pacific Grove, California, USA; 2016. p. 46.
  • Mugler EM, Patton JL, Flint RD, et al. Direct classification of all American English phonemes using signals from functional speech motor cortex. J Neural Eng. 2014 Jun;11(3): 035015. Epub 2014 May 19.10.1088/1741-2560/11/3/035015
  • Bouchard KE, Conant DF, Anumanchipalli GK, et al. High-resolution, non-invasive imaging of upper vocal tract articulators compatible with human brain recordings. PLoS One. 2016 Mar 28;11(3): e0151327.10.1371/journal.pone.0151327
  • Salari E, Freudenburg ZV, Vansteensel MJ, et al. Temporal dynamics of mouth motor cortex activity during speech. Proceedings of the 6th International Brain–computer Interface Meeting; 2016; Pacific Grove, California, USA; 2016. p. 214.
  • Telaar D, Wand M, Gehrig D, et al. BioKIT-real-time decoder for biosignal processing. In: INTERSPEECH 2014; 2014; Singapore. 2014. p. 2650–2654.
  • Herff C, Heger D, de Pesters A, et al. Brain-to-text: decoding spoken phrases from phone representations in the brain. Front Neurosci. 2015 Jun;12(9):217.
  • Martin S, Brunner P, Holdgraf C, et al. Decoding spectrotemporal features of overt and covert speech from the human cortex. Front Neuroeng. 2014 May;27(7):14.
  • Martin S, Brunner P, Iturrate I, et al. Word pair classification during imagined speech using direct brain recordings. Sci Rep. 2016 May 11;6:25803.10.1038/srep25803
  • O’Sullivan JA, Power AJ, Mesgarani N, et al. Attentional selection in a cocktail party environment can be decoded from single-trial EEG. Cereb Cortex. 2015 Jul;25(7):1697–1706.10.1093/cercor/bht355
  • Simon N, Kathner I, Ruf CA, et al. An auditory multiclass Brain–computer interface with natural stimuli: usability evaluation with healthy participants and a motor impaired end user. Front Hum Neurosci. 2015 Jan;9(8):1039.
  • Hill NJ, Scholkopf B An online Brain–computer interface based on shifting attention to concurrent streams of auditory stimuli. J Neural Eng. 2012 Apr;9(2): 026011. Epub 2012 Feb 15.10.1088/1741-2560/9/2/026011
  • Schreuder M, Blankertz B, Tangermann M. A new auditory multi-class Brain–computer interface paradigm: spatial hearing as an information cue. PloS one. 2010;5(4):e9813.10.1371/journal.pone.0009813
  • Hohne J, Schreuder M, Blankertz B, et al. A novel 9-class auditory ERP paradigm driving a predictive text entry system. Front Neurosci. 2011;5:99.
  • Muller KR, Tangermann M, Dornhege G, et al. Machine learning for real-time single-trial EEG-analysis: from Brain–computer interfacing to mental state monitoring. J Neurosci Methods. 2008 Jan 15;167(1):82–90.10.1016/j.jneumeth.2007.09.022
  • Blankertz B, Lemm S, Treder M, et al. Single-trial analysis and classification of ERP components–a tutorial. Neuroimage. 2011 May 15;56(2):814–825.10.1016/j.neuroimage.2010.06.048
  • Mirkovic B, Debener S, Jaeger M, et al. Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications. J Neural Eng. 2015 Aug;12(4): 046007. Epub 2015 Jun 2.10.1088/1741-2560/12/4/046007
  • Bleichner MG, Lundbeck M, Selisky M, et al. Exploring miniaturized EEG electrodes for Brain–computer interfaces. an EEG you do not see? Physiol Rep. 2015 Apr;3(4):10. 14814/phy2.12362.
  • Debener S, Emkes R, De Vos M, et al. Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Sci Rep. 2015 Nov 17;5:16743.10.1038/srep16743
  • Tangermann M, Schnorr N, Musso M. Towards aphasia rehabilitation with BCI. Proceedings of the 6th International Brain–computer Interface Conference, Graz; 2014; Graz, Austria: Verlag der Technischen Universität Graz; 2014. p. 65–68.
  • Schreuder M, Riccio A, Cincotti F, et al. Putting AMUSE to work: an end-user study. Int J Bioelectromagn. 2010;13:139–140.
  • Sturm I, Dähne S, Blankertz B, et al. Multi-variate EEG analysis as a novel tool to examine brain responses to naturalistic music stimuli. PLoS One. 2015 Oct 28;10(10):e0141281.10.1371/journal.pone.0141281
  • Sturm I, Treder M, Miklody D, et al. Extracting the neural representation of tone onsets for separate voices of ensemble music using multivariate EEG analysis. Psychomusicology Music Mind, Brain. 2015;25:366–379.10.1037/pmu0000104
  • Treder MS, Purwins H, Miklody D, et al. Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. J Neural Eng. 2014 Apr;11(2): 026009. Epub 2014 Mar 10.10.1088/1741-2560/11/2/026009
  • Crone NE, Miglioretti DL, Gordon B, et al. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. event-related synchronization in the gamma band. Brain. 1998 Dec;121 (Pt 12):2301–2315.10.1093/brain/121.12.2301
  • Hotson G, McMullen DP, Fifer MS, et al. Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject. J Neural Eng. 2016 Apr;13(2): 026017. Epub 2016 Feb 10.10.1088/1741-2560/13/2/026017
  • Eliseyev A, Aksenova T. Penalized multi-way partial least squares for smooth trajectory decoding from electrocorticographic (ECoG) recording. PLoS One. 2016 May 19;11(5):e0154878.10.1371/journal.pone.0154878
  • Bleichner MG, Freudenburg ZV, Jansma JM, et al. Give me a sign: decoding four complex hand gestures based on high-density ECoG. Brain Struct Funct. 2016 Jan;221(1):203–216.10.1007/s00429-014-0902-x
  • Degenhart AD, Collinger JL, Vinjamuri R, et al. Classification of hand posture from electrocorticographic signals recorded during varying force conditions. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5782–5785.
  • Mestais CS, Charvet G, Sauter-Starace F, et al. WIMAGINE: wireless 64-channel ECoG recording implant for long term clinical applications. IEEE Trans Neural Syst Rehabil Eng. 2015 Jan;23(1):10–21.10.1109/TNSRE.2014.2333541
  • Holz EM, Botrel L, Kaufmann T, et al. Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: A case study. Arch Phys Med Rehabil. 2015 Mar;96(3 Suppl):S16-26.
  • Lance BJ, Touryan J, Wang Y, et al. Towards serious games for improved BCI. In: Nakatsu R, Rauterberg M, Ciancarini P, editors. Handbook of Digital Games and Entertainment Technologies. Singapore: Springer; 2015. p. 1–28. Available from: http://link.springer.com/referenceworkentry/10.1007/978-981-4560-52-8_4-1
  • An X, Kuang D, Guo X, et al. A deep learning method for classification of EEG data based on motor imagery. In: Huang D, Han K, Gromiha M, editors. International Conference on Intelligent Computing; Springer; 2014. p. 203–210. Available from: http://link.springer.com/chapter/10.1007/978-3-319-09330-7_25
  • Hajinoroozi M, Mao Z, Jung T, et al. EEG-based prediction of driver’s cognitive performance by deep convolutional neural network. Signal Processing: Image Communication. 2016 Sep;47:549–555.
  • Hajinoroozi M, Mao Zl, Jung, T-P., et al. Feature extraction with deep belief networks for driver cognitive states prediction from EEG data. 2015 IEEE China Summit International Conference on Signal and Information Processing (ChinaSIP); 2015. p. 812–815.10.1109/ChinaSIP.2015.7230517
  • Mao Z, Lawhern V, Meriño LM, et al. Classification of non-time-locked rapid serial visual presentation events for Brain–computer interaction using deep learning. 2014 IEEE China Summit International Conference on Signal and Information Processing (ChinaSIP); 2014. p. 520–524.10.1109/ChinaSIP.2014.6889297
  • Wang Z, Lyu S, Schalk G, et al. Deep feature learning using target priors with applications in ECoG signal decoding for BCI. Proceedings of the 23rd International Joint Conference on Artificial Intelligence; 2013. p. 1785–1791.
  • Li J, Zhang L. Active training paradigm for motor imagery BCI. Exp Brain Res. 2012 Jun;219(2):245–254.10.1007/s00221-012-3084-x
  • Lawhern V, Slayback D, Wu D, et al. Efficient labeling of EEG signal artifacts using active learning. 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2015. p. 3217–3222.10.1109/SMC.2015.558
  • Marathe AR, Lawhern VJ, Wu D, et al. Improved neural signal classification in a rapid serial visual presentation task using active learning. IEEE Trans Neural Syst Rehabil Eng. 2016 Mar;24(3):333–343.10.1109/TNSRE.2015.2502323
  • Wu D, Lawhern V, Gordon S, et al. Offline EEG-based driver drowsiness estimation using Enhanced Batch-Mode Active Learning (EBMAL) for regression. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2016; Budapest, Hungary; 2016.
  • Wu D, Lance B, Lawhern V. Transfer learning and active transfer learning for reducing calibration data in single-trial classification of visually-evoked potentials. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2014. p. 2801–2807.10.1109/SMC.2014.6974353
  • Ren Y, Wu Y Convolutional deep belief networks for feature extraction of EEG signal. 2014 International Joint Conference on Neural Networks (IJCNN); 2014. p. 2850–2853.10.1109/IJCNN.2014.6889383
  • Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345–1359.10.1109/TKDE.2009.191
  • Kindermans P, Verschore H, Verstraeten D, et al. A P300 BCI for the masses: prior information enables instant unsupervised spelling. Advances in Neural Information Processing Systems (NIPS) 25; 2012; Stateline, NV; 2012. p. 719–727.
  • Lu S, Guan C, Zhang H. Unsupervised brain computer interface based on intersubject information and online adaptation. IEEE Trans Neural Syst Rehabil Eng. 2009 Apr;17(2):135–145.
  • Fazli S, Popescu F, Danoczy M, et al. Subject-independent mental state classification in single trials. Neural Netw. 2009 Nov;22(9):1305–1312.10.1016/j.neunet.2009.06.003
  • Jayaram V, Alamgir M, Altun Y, et al. Transfer learning in Brain–computer interfaces. IEEE Comput Intell Mag. 2016;11(1):20–31.10.1109/MCI.2015.2501545
  • Morioka H, Kanemura A, Hirayama J, et al. Learning a common dictionary for subject-transfer decoding with resting calibration. Neuroimage. 2015 May 1;111:167–178.10.1016/j.neuroimage.2015.02.015
  • Barachant A, Congedo M A plug & play P300 BCI using information geometry. arXiv. 2014; 1409.0107
  • Kindermans PJ, Tangermann M, Muller KR, et al. Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller. J Neural Eng. 2014 Jun;11(3): 035005. doi:10.1088/1741-2560/11/3/035005. Epub 2014 May 19.
  • Samek W, Meinecke FC, Muller KR. Transferring subspaces between subjects in brain–computer interfacing. IEEE Trans Biomed Eng. 2013 Aug;60(8):2289–2298.10.1109/TBME.2013.2253608
  • Bashashati A, Fatourechi M, Ward RK, et al. A survey of signal processing algorithms in Brain–computer interfaces based on electrical brain signals. J Neural Eng. 2007 Jun;4(2):R32–R57.10.1088/1741-2560/4/2/R03
  • Korik A, Siddique N, Sosnik R, et al. Imagined 3D hand movement trajectory decoding from sensorimotor EEG rhythms. IEEE International Conference on Systems, Man, and Cybernetics; 2016; Budapest, Hungary; Forthcoming.
  • Korik A, Siddique N, Sosnik R, et al. 3D hand movement velocity reconstruction using power spectral density of EEG signals and neural network. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2015; Milan; 2015. p. 8103–8106.
  • Zhang Y, Chase SM. Recasting brain–machine interface design from a physical control system perspective. J Comput Neurosci. 2015 Oct;39(2):107–118.10.1007/s10827-015-0566-4
  • Merel J, Pianto DM, Cunningham JP, et al. Encoder-decoder optimization for Brain–computer interfaces. PLoS Comput Biol. 2015 Jun 1;11(6):e1004288.10.1371/journal.pcbi.1004288
  • Merel J, Carlson D, Paninski L, et al. Neuroprosthetic decoder training as imitation learning. PLoS Comput Biol. 2016 May 18;12(5):e1004948.10.1371/journal.pcbi.1004948
  • Schaffelhofer S, Agudelo-Toro A, Scherberger H. Decoding a wide range of hand configurations from macaque motor, premotor, and parietal cortices. J Neurosci. 2015 Jan 21;35(3):1068–1081.10.1523/JNEUROSCI.3594-14.2015
  • Shanechi MM, Orsborn AL, Carmena JM. Robust brain–machine interface design using optimal feedback control modeling and adaptive point process filtering. PLoS Comput Biol. 2016 Apr 1;12(4):e1004730.10.1371/journal.pcbi.1004730
  • Gilja V, Pandarinath C, Blabe CH, et al. Clinical translation of a high-performance neural prosthesis. Nat Med. 2015 Oct;21(10):1142–1145.10.1038/nm.3953
  • Nuyujukian P, Fan JM, Kao JC, et al. A high-performance keyboard neural prosthesis enabled by task optimization. IEEE Trans Biomed Eng. 2015 Jan;62(1):21–29.10.1109/TBME.2014.2354697
  • Chavarriaga R, Biasiucci A, Molina A, et al. tDCS modulates motor imagery-related BCI features. In: Pons JL, Torricelli D, Pajaro M, editors. Converging Clinical and Engineering Research on Neurorehabilitation. Berlin Heidelberg: Springer; 2013. p. 647–651.
  • Soekadar SR, Witkowski M, Robinson SE, et al. Combining electric brain stimulation and source-based brain–machine interface (BMI) training in neurorehabilitation of chronic stroke. J Neurol Sci. 2013;333:e542.
  • Hsu WY, Ku Y, Zanto TP, et al. Effects of noninvasive brain stimulation on cognitive function in healthy aging and alzheimer’s disease: a systematic review and meta-analysis. Neurobiol Aging. 2015 Aug;36(8):2348–2359.10.1016/j.neurobiolaging.2015.04.016
  • Ruffini G, Wendling F, Merlet I, et al. Transcranial current brain stimulation (tCS): models and technologies. IEEE Trans Neural Syst Rehabil Eng. 2013 May;21(3):333–345.10.1109/TNSRE.2012.2200046
  • Witkowski M, Garcia-Cossio E, Chander BS, et al. Mapping entrained brain oscillations during transcranial alternating current stimulation (tACS). Neuroimage. 2015 Oct;140:89–98.
  • Ruffini G, Fox MD, Ripolles O, et al. Optimization of multifocal transcranial current stimulation for weighted cortical pattern targeting from realistic modeling of electric fields. Neuroimage. 2014 Apr 1;89:216–225.10.1016/j.neuroimage.2013.12.002
  • Chatterjee A, Aggarwal V, Ramos A, et al. A Brain–computer interface with vibrotactile biofeedback for haptic information. J Neuroeng Rehabil. 2007 Oct 17;4:40.10.1186/1743-0003-4-40
  • Rutkowski TM, Mori H. Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users. J Neurosci Methods. 2015 Apr 15;244:45–51.10.1016/j.jneumeth.2014.04.010
  • Brouwer A-M, Van Erp JBF. A tactile P300 Brain–computer interface. Front Neurosci. 2010;5:12.
  • Ziat M Haptics and HCI. foundations and trends in human-computer interaction. (in press).
  • Morrow K, Wilbern D, Taghavi R, et al. The effects of duration and frequency on the perception of vibrotactile stimulation on the neck. 2016 IEEE Haptics Symposium; 2016; Philadelphia; 2016. p. 41–46.
  • Morrow K, Wilbern D, Taghavi R, et al. Vestibular-tactile substitution: vertical posture correction using vibrotactile stimulation on the neck. 2016 IEEE Haptics Symposium; 2016; Philadelphia, PA; 2016.
  • Beaudoin N, De Serres L, Martel N, et al. Medical aspect, communication and quality of life after locked-in syndrome, a review of twenty cases. Stroke. 2010;41(7):E491.
  • Sketch SM, Deo DR, Menon JP, et al. Design and experimental evaluation of a skin-stretch haptic device for improved control of Brain–computer interfaces. IEEE International Conference on Robotics and Automation; 2015. p. 272–277.
  • Gleeson BT, Horschel SK, Provancher WR. Design of a fingertip-mounted tactile display with tangential skin displacement feedback. IEEE Trans Haptics. 2010;3(4):297–301.10.1109/TOH.2010.8
  • Ang KK, Guan C. Brain–computer interface for neurorehabilitation of upper limb after stroke. Proc IEEE. 2015;103:944–953.10.1109/JPROC.2015.2415800
  • Wagner J, Solis-Escalante T, Grieshofer P, et al. Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. Neuroimage. 2012 Nov 15;63(3):1203–1211.10.1016/j.neuroimage.2012.08.019
  • Scherer R, Billinger M, Wagner J, et al. Thought-based row-column scanning communication board for individuals with cerebral palsy. Ann Phys Rehabil Med. 2015 Feb;58(1):14–22.10.1016/j.rehab.2014.11.005
  • Debener S, Minow F, Emkes R, et al. How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology. 2012 Nov;49(11):1617–1621.10.1111/psyp.2012.49.issue-11
  • Park JL, Fairweather MM, Donaldson DI. Making the case for mobile cognition: EEG and sports performance. Neurosci Biobehav Rev. 2015 May;52:117–130.10.1016/j.neubiorev.2015.02.014
  • De Vos M, Debener S. Mobile EEG: towards brain activity monitoring during natural action and cognition. Int J Psychophysiol. 2014 Jan;91(1):1–2.10.1016/j.ijpsycho.2013.10.008
  • Daly I, Scherer R, Billinger M, et al. FORCe: fully online and automated artifact removal for Brain–computer interfacing. IEEE Trans Neural Syst Rehabil Eng. 2015 Sep;23(5):725–736.10.1109/TNSRE.2014.2346621
  • Seeber M, Scherer R, Wagner J, et al. High and low gamma EEG oscillations in central sensorimotor areas are conversely modulated during the human gait cycle. Neuroimage. 2015 May 15;112:318–326.10.1016/j.neuroimage.2015.03.045
  • Congedo M, Gouy-Pailler C, Jutten C. On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clin Neurophysiol. 2008 Dec;119(12):2677–2686.10.1016/j.clinph.2008.09.007
  • Barachant A, Bonnet S, Congedo M, et al. Classification of covariance matrices using a riemannian-based kernel for BCI applications. Neurocomputing. 2013;112:172–178.10.1016/j.neucom.2012.12.039
  • ANT Neuro, Enschede, Netherlands.
  • McFarland DJ, Wolpaw JR. Brain–computer interfaces for communication and control. Commun ACM. 2011;54(5):60–66.10.1145/1941487
  • Chaudhary U, Birbaumer N, Curado MR. Brain–machine interface (BMI) in paralysis. Ann Phys Rehabil Med. 2015 Feb;58(1):9–13.10.1016/j.rehab.2014.11.002
  • Lee JH, Ryu J, Jolesz FA, et al. Brain–machine interface via real-time fMRI: preliminary study on thought-controlled robotic arm. Neurosci Lett. 2009 Jan 23;450(1):1–6.10.1016/j.neulet.2008.11.024
  • Weiskopf N, Mathiak K, Bock SW, et al. Principles of a Brain–computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng. 2004 Jun;51(6):966–970.10.1109/TBME.2004.827063
  • Gallegos-Ayala G, Furdea A, Takano K, et al. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology. 2014 May 27;82(21):1930–1932.10.1212/WNL.0000000000000449
  • Naseer N, Hong KS. fNIRS-based Brain–computer interfaces: a review. Front Hum Neurosci. 2015 Jan;28(9):3.
  • Murphy MD, Guggenmos DJ, Bundy DT, et al. Current challenges facing the translation of brain computer interfaces from preclinical trials to use in human patients. Front Cell Neurosci. 2016 Jan;6(9):497.
  • Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive Brain–computer interface. Proc Nat Acad Sci. 2015 Nov 3;112(44):E6058–E6067.10.1073/pnas.1508080112
  • Pandarinath C, Nuyujukian P, Gilja V, et al. Application of a high performance intracortical brain computer interface for communication in a person with amyotrophic lateral sclerosis. Program No. 252.08. Neuroscience Meeting Planner. Washington, DC: Society for Neuroscience; 2014. Online.
  • Kao JC, Nuyujukian P, Ryu SI, et al. Single-trial dynamics of motor cortex and their applications to brain–machine interfaces. Nat Commun. 2015 Jul 29;6:7759.10.1038/ncomms8759
  • Pandarinath C, Gilja V, Blabe CH, et al. Neural population dynamics in human motor cortex during movements in people with ALS. Elife. 2015 Jun;23(4):e07436.
  • Willett FR, Pandarinath C, Jarosiewicz B, et al. Feedback control policies employed by people using intracortical brain-computer interfaces. J Neural Eng. 2016 Nov;14(1):016001.
  • Sellers EW, Vaughan TM, Wolpaw JR. A Brain–computer interface for long-term independent home use. Amyotroph Lateral Scler. 2010 Jun;11(5):449–455.
  • McCane LM, Heckman SM, McFarland DJ, et al. P300-based Brain–computer interface (BCI) event-related potentials (ERPs): people with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. Clin Neurophysiol. 2015 Feb;11(5):449–455
  • Sellers EW, Ryan DB, Hauser CK. Noninvasive Brain–computer interface enables communication after brainstem stroke. Sci Transl Med. 2014 Oct 8;6(257):257re7.10.1126/scitranslmed.3007801
  • Miralles F, Vargiu E, Dauwalder S, et al. Brain computer interface on track to home. Sci World J. 2015;2015:623896.
  • Jarosiewicz B, Sarma AA, Bacher D, et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical Brain–computer interface. Sci Transl Med. 2015 Nov 11;7(313):313ra179.10.1126/scitranslmed.aac7328
  • Blabe CH, Gilja V, Chestek CA, et al. Assessment of brain–machine interfaces from the perspective of people with paralysis. J Neural Eng. 2015 Aug;12(4): 043002. Epub 2015 Jul 14.10.1088/1741-2560/12/4/043002
  • Mudry A, Mills M. The early history of the cochlear implant: a retrospective. JAMA Otolaryngol Head Neck Surg. 2013 May;139(5):446–453.10.1001/jamaoto.2013.293
  • Vlek RJ, Steines D, Szibbo D, et al. Ethical issues in Brain–computer interface research, development, and dissemination. J Neurol Phys Ther. 2012 Jun;36(2):94–99.10.1097/NPT.0b013e31825064cc
  • Ramirez R, Palencia-Lefler M, Giraldo S, et al. Musical neurofeedback for treating depression in elderly people. Front Neurosci. 2015 Oct;2(9):354.
  • Stanslaski S, Afshar P, Cong P, et al. Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. IEEE Trans Neural Syst Rehabil Eng. 2012 Jul;20(4):410–421.10.1109/TNSRE.2012.2183617
  • Charvet G, Sauter-Starace F, Foerster M, et al. WIMAGINE((R)): 64-channel ECoG recording implant for human applications. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:2756–2759.
  • Ando H, Takizawa K, Yoshida T, et al. Wireless multichannel neural recording with a 128-mbps UWB transmitter for an implantable brain–machine interfaces. IEEE Trans Biomed Circuits Syst. 2016 Feb;10(6):1068–1078.
  • Hirata M, Matsushita K, Suzuki T, et al. A fully-implantable wireless system for human brain–machine interfaces using brain surface electrodes: W-HERBS. IEICE Trans Commun. 2011;94(9):2448–2453.10.1587/transcom.E94.B.2448
  • Borton DA, Song YK, Patterson WR, et al. Wireless, high-bandwidth recordings from non-human primate motor cortex using a scalable 16-ch implantable microsystem. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:5531–5534.
  • Borton DA, Yin M, Aceros J, et al. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J Neural Eng. 2013 Apr;10(2): 026010. Epub 2013 Feb 21.10.1088/1741-2560/10/2/026010
  • Vansteensel MJ, Pels EGM, Bleichner MG, et al. Fully implanted Brain–computer interface in a locked-in patient with ALS. N Engl J Med. 2016;375(21):2060–2066.
  • Aarnoutse EJ, Vansteensel MJ, Pels EGM, et al. Demonstration of minimal invasive surgery for an invisible, permanent Brain–computer Interface. Neuroscience Meeting Planner. Society for Neuroscience, Online; 2016; Washington, DC, USA; 2016. abstract number 58.15.
  • Chavarriaga R, Fried-Oken M, Kleih S, et al. Heading for new shores! overcoming pitfalls in BCI design brain-computer interfaces. Brain Comp Inter. 2017 (in press).
  • Lotte F, Larrue F, Muhl C. Flaws in current human training protocols for spontaneous Brain–computer interfaces: lessons learned from instructional design. Front Hum Neurosci. 2013 Sep;17(7):568.
  • Kubler A, Holz EM, Riccio A, et al. The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications. PLoS One. 2014 Dec 3;9(12):e112392.10.1371/journal.pone.0112392
  • Uriguen JA, Garcia-Zapirain B. EEG artifact removal-state-of-the-art and guidelines. J Neural Eng. 2015 Jun;12(3): 031001. Epub 2015 Apr 2.10.1088/1741-2560/12/3/031001
  • Daly I, Billinger M, Laparra-Hernandez J, et al. On the control of Brain–computer interfaces by users with cerebral palsy. Clin Neurophysiol. 2013 Sep;124(9):1787–1797.10.1016/j.clinph.2013.02.118
  • Gwin JT, Gramann K, Makeig S, et al. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol. 2010 Jun;103(6):3526–3534.10.1152/jn.00105.2010
  • Thomas E, Dyson M, Clerc M An analysis of performance evaluation for motor-imagery based BCI. J Neural Eng. 2013 Jun;10(3): 031001. Epub 2013 May 3.10.1088/1741-2560/10/3/031001
  • Hill NJ, Hauser AK, Schalk G A general method for assessing Brain–computer interface performance and its limitations. J Neural Eng. 2014 Apr;11(2): 026018. Epub 2014 Mar 24.10.1088/1741-2560/11/2/026018
  • Nijboer F, Birbaumer N, Kubler A. The influence of psychological state and motivation on Brain–computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study. Front Neurosci. 2010 Jul;21(4):55.
  • Kleih SC, Nijboer F, Halder S, et al. Motivation modulates the P300 amplitude during Brain–computer interface use. Clin Neurophysiol. 2010;2010.
  • Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods. 2007 Aug 15;164(1):177–190.10.1016/j.jneumeth.2007.03.024
  • Müller-Putz G, Scherer R, Brunner C, et al. Better than random: a closer look on BCI results. Int J Bioelectromagn. 2008;10:52–55.
  • Wasserstein RL, Lazar NA. The ASA’s statement on p-values: context, process, and purpose, taylor & francis. Am Stat. 2016;70(2):129–133.10.1080/00031305.2016.1154108
  • Lemm S, Blankertz B, Dickhaus T, et al. Introduction to machine learning for brain imaging. Neuroimage. 2011 May 15;56(2):387–399.10.1016/j.neuroimage.2010.11.004
  • Nuzzo R. How scientists fool themselves - and how they can stop. Nature. 2015 Oct 8;526(7572):182–185.10.1038/526182a
  • Kass RE, Caffo BS, Davidian M, et al. Ten simple rules for effective statistical practice. PLoS Comput Biol. 2016 Jun 9;12(6):e1004961.10.1371/journal.pcbi.1004961
  • Duncan CC, Barry RJ, Connolly JF, et al. Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clin Neurophysiol. 2009 Nov;120(11):1883–1908.10.1016/j.clinph.2009.07.045
  • Brouwer AM, Zander TO, van Erp JB, et al. Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls. Front Neurosci. 2015 Apr;30(9):136.