Development of speech prostheses: current status and recent advances

References

• Plum F, Posner JB. The diagnosis of stupor and coma. Contemp. Neurol. Ser.10, 1–286 (1972).
   PubMedGoogle Scholar
• Haig AJ, Katz RT, Sahgal V. Mortality and complications of the locked-in syndrome. Arch. Phys. Med. Rehabil.68, 24–27 (1987).
   PubMed Web of Science ®Google Scholar
• Katz RT, Haig AJ, Clark BB, DiPaola RJ. Long-term survival, prognosis, and life-care planning for 29 patients with chronic locked-in syndrome. Arch. Phys. Med. Rehabil.73, 403–408 (1992).
   PubMed Web of Science ®Google Scholar
• Doble JE, Haig AJ, Anderson C, Katz R. Impairment, activity, participation, life satisfaction, and survival in persons with locked-in syndrome for over a decade: follow-up on a previously reported cohort. J. Head Trauma Rehabil.18, 435–444 (2003).
   PubMed Web of Science ®Google Scholar
• Smith E, Delargy M. Locked-in syndrome. BMJ330, 406–409 (2005).
   PubMed Web of Science ®Google Scholar
• Birbaumer N, Ghanayim N, Hinterberger T et al. A spelling device for the paralysed. Nature398, 297–298 (1999).
   PubMed Web of Science ®Google Scholar
• Birbaumer N, Kubler A, Ghanayim N et al. The thought translation device (TTD) for completely paralyzed patients. IEEE Trans. Neural. Syst. Rehabil. Eng.8, 190–193 (2000).
   Google Scholar
• Hinterberger T, Kübler A, Kaiser J, Neumann N, Birbaumer N. A brain-computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device. Clin. Neurophysiol.114, 416–425 (2003).
   PubMed Web of Science ®Google Scholar
• Birbaumer N, Hinterberger T, Kübler A, Neumann N. The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans. Neural. Syst. Rehabil. Eng.11, 120–123 (2003).
   PubMed Web of Science ®Google Scholar
• Kübler A, Kotchoubey B, Hinterberger T et al. The thought translation device: a neurophysiological approach to communication in total motor paralysis. Exp. Brain Res.124, 223–232 (1999).
   PubMed Web of Science ®Google Scholar
• Farwell L, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Clin. Neurophysiol.70, 510–523 (1988).
   Google Scholar
• Donchin E, Spencer K, Wijesinghe R. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Neural. Syst. Rehabil. Eng.8, 174–179 (2000).
   Google Scholar
• Krusienski DJ, Sellers EW, Cabestaing F et al. A comparison of classification techniques for the P300 Speller. J. Neural Eng.3, 299–305 (2006).
   PubMed Web of Science ®Google Scholar
• Nijboer F, Sellers E, J Mellinger et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin. Neurophysiol.119, 1909–1916 (2008).
   PubMed Web of Science ®Google Scholar
• Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR. Toward enhanced P300 speller performance. J. Neurosci. Methods167, 15–21 (2008).
   PubMed Web of Science ®Google Scholar
• Sellers EW, Krusienski DJ, McFarland DJ, Vaughan TM, Wolpaw JR. A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol. Psychol.73, 242–252 (2006).
   PubMed Web of Science ®Google Scholar
• Sellers EW, Donchin E. A P300-based brain-computer interface: initial tests by patients ALS. Clin. Neurophysiol.117, 538–548 (2006).
   PubMed Web of Science ®Google Scholar
• Sutter EE. The brain response interface: communication through visually-induced electrical brain responses. J. Microcomputer. App.15, 31–45 (1992).
   Google Scholar
• Cheng M, Gao X, Gao S, Xu D. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans. Biomed. Eng.49, 1181–1186 (2002).
   PubMed Web of Science ®Google Scholar
• Friman O, Luth T, Volosyak I, Graser A. Spelling with steady-state visual evoked potentials. 3rd International IEEE/EMBS Conference on Neural Engineering, 2007. CNE 07, 354–357 (2007).
   Google Scholar
• Miner LA, McFarland DJ, Wolpaw JR. Answering questions with an electroencephalogram-based brain–computer interface. Arch. Phys. Med. Rehabil.79, 1029–1033 (1998).
   PubMed Web of Science ®Google Scholar
• Vaughan TM, McFarland DJ, Schalk G, Sarnacki WA. EEG-based brain-computer interface: development of a speller. Neurocience Meeting Planner 2001, San Diego, CA, USA (2001).
   Google Scholar
• Vaughan T, McFarland D, Schalk G et al. The wadsworth BCI research and development program: at home with BCI. IEEE Trans. Neural. Syst. Rehabil. Eng.14, 229–233 (2006).
   PubMed Web of Science ®Google Scholar
• Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE89, 1123–1134 (2001).
   Web of Science ®Google Scholar
• Neuper C, Müller-Putz GR, Scherer R, Pfurtscheller G. Motor imagery and EEG-based control of spelling devices and neuroprostheses. Event-Related Dynamics of Brain Oscillations. Elsevier, 393–409 (2006).
   Google Scholar
• Obermaier B, Muller G, Pfurtscheller G. “Virtual keyboard” controlled by spontaneous activity EEG. IEEE Trans. Neural. Syst. Rehabil. Eng.11, 422–426 (2003).
   Google Scholar
• Scherer R, Muller G, Neuper C, Graimann B, Pfurtscheller G. An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans. Biomed. Eng.51, 979–984 (2004).
   PubMed Web of Science ®Google Scholar
• Neuper C, Müller GR, Kübler A, Birbaumer N, Pfurtscheller G. Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment. Clin. Neurophysiol.114, 399–409 (2003).
   PubMed Web of Science ®Google Scholar
• Blankertz B, Dornhege G, Lemm S, Krauledat M, Curio G, Müller K. The Berlin brain–computer interface: machine learning based detection of user specific brain states. J. Univers. Comput. Sci.12, 581–607 (2006).
   Google Scholar
• Blankertz B, Tangermann M, Popescu F et al. The Berlin brain–computer interface. Lect. Notes Comput. Sci.79, 5050 (2008).
   Google Scholar
• Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, Müller K. The Berlin brain–computer interface: accurate performance from first-session in BCI-naïve subjects. IEEE Trans. Biomed. Eng.55, 2452–2462 (2008).
   PubMed Web of Science ®Google Scholar
• Blankertz B, Krauledat M, Dornhege G, Williamson J, Murray-Smith R, Müller K. A note on brain actuated spelling with the Berlin brain–computer interface. Universal Access in Human-Computer Interaction. Ambient Interaction. Springer-Verlag. Berlin, Heidelberg. 759–768 (2007).
   Google Scholar
• Hinterberger T, Widmann G, Lal TN et al. Voluntary brain regulation and communication with ECoG-signals. Epilepsy Behav.13, 300–306 (2008).
   Google Scholar
• Suppes P, Lu Z, Han B. Brain wave recognition of words. Proc. Natl Acad. Sci. USA94, 14965–14969 (1997).
   PubMed Web of Science ®Google Scholar
• Suppes P, Han B. Brain-wave representation of words by superposition of a few sine waves. Proc. Natl Acad. Sci. USA97, 8738–8743 (2000).
   Google Scholar
• Guimaraes M, Wong DK, Uy E, Grosenick L, Suppes P. Single-trial classification of recordings MEG. IEEE Trans. Biomed. Eng.54, 436–443 (2007).
   PubMed Web of Science ®Google Scholar
• DaSalla CS, Kambara H, Sato M, Koike Y. Single-trial classification of vowel speech imagery using common spatial patterns. Neural Netw.22, 1334–1339 (2009).
   PubMed Web of Science ®Google Scholar
• Schalk G, Barbour D, Leuthardt EC, Pei X. Decoding spoken and imagined word groups using electrocorticographic signals in humans. Neuroscience Meeting Planner 2009. IL, USA (2009).
   Google Scholar
• Leuthardt EC, Freudenberg Z, Gaona C et al. Microscale electrocorticographic recording from human cortex and neuroprosthetic implications. Neuroscience Meeting Planner 2009. IL, USA (2009).
   Google Scholar
• Brumberg JS, Kennedy PR, Guenther FH. Artificial Speech Synthesizer Control by Brain-Computer Interface. Proceedings of the 10th Annual Conference of the International Speech Communication Association. Brighton, UK: International Speech Communication Association (2009).
   Google Scholar
• Guenther FH, Brumberg JS, Wright EJ et al. A wireless brain-machine interface for real-time speech synthesis. PLoS ONE,4, E8218 (2009).
   PubMed Web of Science ®Google Scholar
• Brumberg JS, Nieto-Castanon A, Kennedy PR, Guenther FH. Brain-computer interfaces for speech communication. Speech Commun.52, 367–379 (2010).
   PubMed Web of Science ®Google Scholar
• Kennedy PR. In: Comparing electrodes for use as cortical control signals: tiny tines, tiny wires or tiny cones on wires: Which is best? The Biomedical Engineering Handbook, Boca Raton: CRS/Taylor and Francis (2006).
   Google Scholar
• Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl Acad. Sci. USA101, 17849–17854 (2004).
   PubMed Web of Science ®Google Scholar
• Wolpaw J, McFarland D, Vaughan T. Brain-computer interface research at the Wadsworth Center. IEEE Trans. Neural. Syst. Rehabil. Eng.8, 222–226 (2000).
   Google Scholar
• Wolpaw J, McFarland D, Vaughan T, Schalk G. The Wadsworth Center brain-computer interface (BCI) research and development program. IEEE Trans. Neural. Syst. Rehabil. Eng.11, 1–4 (2003).
   PubMed Web of Science ®Google Scholar
• Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW. A brain-computer interface using electrocorticographic signals in humans. J. Neural Eng.1, 63–71 (2004).
   PubMed Web of Science ®Google Scholar
• Schalk G, Kubánek J, Miller KJ et al. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J. Neural Eng.4, 264–275 (2007).
   PubMed Web of Science ®Google Scholar
• Schalk G, Miller KJ, Anderson NR et al. Two-dimensional movement control using electrocorticographic signals in humans. J. Neural Eng.5, 75–84 (2008).
   PubMed Web of Science ®Google Scholar
• Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP. Instant neural control of a movement signal. Nature416, 141–142 (2002).
   PubMed Web of Science ®Google Scholar
• Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP. Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J. Neurophysiol.91, 515–532 (2004).
   PubMed Web of Science ®Google Scholar
• Taylor DM, Tillery SI, Schwartz AB. Direct cortical control of 3D neuroprosthetic devices. Science296, 1829–1832 (2002).
   PubMed Web of Science ®Google Scholar
• Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB. Cortical control of a prosthetic arm for self-feeding. Nature453, 1098–1101 (2008).
   PubMed Web of Science ®Google Scholar
• Chapin JK, Moxon KA, Markowitz RS, Nicolelis MAL. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci.2, 664–670 (1999).
   PubMed Web of Science ®Google Scholar
• Wessberg J, Stambaugh CR, Kralik JD et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature408, 361–365 (2000).
   PubMed Web of Science ®Google Scholar
• Carmena JM, Lebedev MA, Crist RE et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol.1, 193–208 (2003).
   Web of Science ®Google Scholar
• Kim S, Simeral JD, Hochberg LR, Donoghue JP, Friehs GM, Black MJ. Multi-state decoding of point-and-click control signals from motor cortical activity in a human with tetraplegia. 3rd International IEEE/EMBS Conference Neural Engineering 2007, CNE’07. 486–489 (2007).
   Google Scholar
• Donoghue JP, Nurmikko A, Black M, Hochberg LR. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J. Physiol.579, 603–611 (2007).
   PubMed Web of Science ®Google Scholar
• Hochberg LR, Simeral JD, Kim S et al. More than 2 years of intracortically-based cursor control via a neural interface system. Neurosicence Meeting Planner 2008. Washington, DC, USA (2008).
   Google Scholar
• Hochberg LR, Serruya MD, Friehs GM et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature442, 164–171 (2006).
   PubMed Web of Science ®Google Scholar
• Lewicki MS. A review of methods for spike sorting: the detection and classification of neural action potentials. Network: Computation Neur. Syst.9, R53–R78 (1998).
   PubMed Web of Science ®Google Scholar
• Wise KD, Angell JB, Starr A. An integrated-circuit approach to extracellular microelectrodes. IEEE Trans. Biomed. Eng.17(3), 238–247 (1970).
   PubMedGoogle Scholar
• Hoogerwerf A, Wise KA. Three-dimensional microelectrode array for chronic neural recording. IEEE Trans. Biomed. Eng.41, 1136–1146 (1994).
   Google Scholar
• Nicolelis MAL, Dimitrov D, Carmena JM et al. Chronic, multisite, multielectrode recordings in macaque monkeys. PNAS100, 11041–11046 (2003).
   PubMed Web of Science ®Google Scholar
• Kennedy PR. The cone electrode: a long-term electrode that records from neurites grown onto its recording surface. J. Neurosci. Methods29, 181–193 (1989).
   PubMed Web of Science ®Google Scholar
• Bartels JL, Andreasen D, Ehirim P et al. Neurotrophic electrode: method of assembly and implantation into human motor speech cortex. J. Neurosci. Methods174, 168–176 (2008).
   PubMed Web of Science ®Google Scholar
• Jones K, Campbell P, Normann R. A glass/silicon composite intracortical electrode array. Ann. Biomed. Eng.20, 423–437 (1992).
   PubMed Web of Science ®Google Scholar
• Maynard EM, Nordhausen CT, Normann RA. The Utah Intracortical electrode array: a recording structure for potential brain-computer interfaces. Clin. Neurophysiol.102, 228–239 (1997).
   Google Scholar
• Rousche PJ, Normann RA. Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex. J. Neurosci. Methods82, 1–15 (1998).
   PubMedGoogle Scholar
• Allison B, Pineda J. ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system. IEEE Trans. Neural. Syst. Rehabil. Eng.11, 110–113 (2003).
   PubMed Web of Science ®Google Scholar
• Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller K. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Magazine Proc.25, 581–607 (2008).
   Google Scholar
• Krauledat M, Tangermann M, Blankertz B, Müller K. Towards zero training for brain-computer interfacing. PLoS ONE3, E2967 (2008).
   Google Scholar
• Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Neural. Syst. Rehabil. Eng.8, 441–446 (2000).
   Google Scholar
• Georgopoulos AP, Schwartz AB, Kettner RE. Neuronal population coding of movement direction. Science233, 1416–1419 (1986).
   PubMed Web of Science ®Google Scholar
• Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ. Bayesian population decoding of motor cortical activity using a kalman filter. Neural Comput.18, 80–118 (2006).
   PubMed Web of Science ®Google Scholar
• Kennedy PR, Bakay RAE. Restoration of neural output from a paralyzed patient by direct brain connection. Neuroreport9, 1707–1711 (1998).
   PubMed Web of Science ®Google Scholar
• Kennedy PR, Mirra SS, Bakay RAE. The cone electrode: ultrastructural studies following long-term recording in rat and monkey cortex. Neuroscience Lett.142, 89–94 (1992).
   PubMedGoogle Scholar
• Kennedy PR, Bakay RAE, Moore MM, Adams K, Goldwaithe J. Direct control of a computer from the human central nervous system. IEEE Trans. Neural. Syst. Rehabil. Eng.8, 198–202 (2000).
   Google Scholar
• Kennedy PR, Kirby T, Moore MM, King B, Mallory A. Computer control using human intracortical local field potentials. IEEE Trans. Neural. Syst. Rehabil. Eng.12, 339–344 (2004).
   PubMed Web of Science ®Google Scholar
• Wright EJ, Andreasen DS, Bartels JL et al. Human speech cortex long-term recordings: neural net analyses. Neuroscience Meeting Planner 2007. CA, USA (2007).
   Google Scholar
• Miller LE, Andreasen DS, Bartels JL et al. Human speech cortex long-term recordings: Bayesian analyses. Neuroscience Meeting Planner 2007. CA, USA (2007).
   Google Scholar
• Brumberg JS, Andreasen DS, Bartels JL et al. Human speech cortex long-term recordings: formant frequency analyses. Neuroscience Meeting Planner 2007. CA, USA (2007).
   Google Scholar
• Maeda S. Compensatory articulation during speech: evidence from the analysis and sythesis of vocal tract shapes using an articulatory model. In: Speech Production and Speech Modeling. Boston: Kluwer Academic Publishers, USA (1990).
   Google Scholar
• Kalman RE. A new approach to linear filtering and prediction problems. J. Basic Biol. Eng.82, 35–45 (1960).
   Google Scholar