Challenges of implementing a personalized mental task near-infrared spectroscopy brain–computer interface for a non-verbal young adult with motor impairments

References

• Blain S, Mihailidis A, Chau T. Assessing the potential of electrodermal activity as an alternative access pathway. Medical Engineering & Physics 2008;30:498–505
   PubMed Web of Science ®Google Scholar
• Kübler A, Kotchoubey B, Kaiser J, Wolpaw JR, Birbaumer N. Brain–computer communication: Unlocking the locked in. Psychological Bulletin 2001;127:358–375
   PubMed Web of Science ®Google Scholar
• Fenton A, Alpert S. Extending our view on using BCIS for locked-in syndrome. Neuroethics 2008;1:119–132
   Web of Science ®Google Scholar
• Bach CA, McDaniel RW. Quality of life in quadriplegic adults: A focus group study. Rehabilitation Nursing 1993;18:364–367, 374
   PubMedGoogle Scholar
• Sitaram R, Zhang H, Guan C, Thulasidas M, Hoshi Y, Ishikawa A, Shimizu K, Birbaumer N. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface. NeuroImage 2007;34:1416–1427
   PubMed Web of Science ®Google Scholar
• Tai K, Blain S, Chau T. A review of emerging access technologies for individuals with severe motor impairments. Assistive Technology 2008;20:204–219
   PubMed Web of Science ®Google Scholar
• Wolpaw J, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson C, Vaughan TM. Brain–computer interface technology: A review of the first international meeting. IEEE Transactions on Rehabilitation Engineering 2000;8:164–173
   PubMedGoogle Scholar
• Birbaumer N, Cohen LG. Brain–computer interfaces: Communication and restoration of movement in paralysis. The Journal of Physiology 2007;579:621–636
   PubMed Web of Science ®Google Scholar
• Coyle SM, Ward TE, Markham CM. Brain–computer interface using a simplified functional near-infrared spectroscopy system. Journal of Neural Engineering 2007;4:219–226
   PubMed Web of Science ®Google Scholar
• Ayaz H, Izzetoglu M, Bunce S, Heiman-Patterson T, Onaral B. Detecting cognitive activity related hemodynamic signal for brain–computer interface using functional near infrared spectroscopy. In: 3rd International IEEE/EMBS Conference on Neural Engineering. IEEE; Kohala Coast, Hawaii. 2007. pp 342–345
   Google Scholar
• Grosse-wentrup M, Schölkopf B. A review of performance variations in SMR-based brain−computer interfaces (BCIs). In: Guger C, Allison BZ, Edlinger G, editors. Brain–computer interface research: A state-of-the-art summary. Springer Briefs in Electrical and Computer Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013. pp 39–51
   Google Scholar
• Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kuebler A, Perelmouter J, Taub E, Flor H. A spelling device for the paralysed. Nature 1999;398:297–298
   PubMed Web of Science ®Google Scholar
• Kübler A, Kotchoubey B, Hinterberger T, Ghanayim N, Perelmouter J, Schauer M, Fritsch C, Taub E, Birbaumer N. The thought translation device: A neurophysiological approach to communication in total motor paralysis. Experimental Brain Research 1999;124:223–232
   PubMed Web of Science ®Google Scholar
• Pfurtscheller G, Neuper C, Müller GR, Obermaier B, Krausz G, Schlögl A, Scherer R, Graimann B, Keinrath C, Skliris D, et al. Graz-BCI: State of the art and clinical applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2003;11:177–180
   PubMed Web of Science ®Google Scholar
• Birbaumer N, Kübler A, Ghanayim N, Hinterberger T, Perelmouter J, Kaiser J, Iversen I, Kotchoubey B, Neumann N, Flor H. The thought translation device (TTD) for completely paralyzed patients. IEEE Transactions on Rehabilitation Engineering 2000;8:190–193
   PubMedGoogle Scholar
• Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proceedings of the National Academy of Sciences of the United States of America 2004;101:17849–17854
   PubMed Web of Science ®Google Scholar
• Kübler A, Furdea A, Halder S, Hammer EM, Nijboer F, Kotchoubey B. A brain–computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Annals of the New York Academy of Sciences 2009;1157:90–100
   PubMed Web of Science ®Google Scholar
• Kübler A, Neumann N, Kaiser J, Kotchoubey B, Hinterberger T, Birbaumer NP. Brain–computer communication: Self-regulation of slow cortical potentials for verbal communication. Archives of Physical Medicine and Rehabilitation 2001;82:1533–1539
   PubMed Web of Science ®Google Scholar
• Kübler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, Mcfarland DJ, Birbaumer N, Wolpaw JR. Patients with ALS can use sensorimotor rhythms to operate a brain. Neurology 2005;64:1775–1777
   PubMed Web of Science ®Google Scholar
• Birbaumer N, Elber T, Rockstroh B, Lutzenberger W. Biofeedback of event-related slow potentials of the brain. International Journal of Psychology 1981;16:389–415
   Web of Science ®Google Scholar
• Holz EM, Höhne J, Staiger-Sälzer P, Tangermann M, Kübler A. Brain–computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users. Artificial Intelligence in Medicine 2013;59:111–120
   PubMed Web of Science ®Google Scholar
• Moghimi S, Kushki A, Guerguerian AM, Chau T. A review of EEG-based brain–computer interfaces as access pathways for individuals with severe disabilities. Assistive Technology 2012;25:99–110
   Web of Science ®Google Scholar
• Naito M, Michioka Y, Ozawa K, Ito Y, Kiguchi M, Kanazawa T. A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE – Transactions on Information and Systems 2007;E90-D:1028–1037
   Web of Science ®Google Scholar
• Power SD, Chau T. Automatic single-trial classification of prefrontal hemodynamic activity in an individual with Duchenne muscular dystrophy. Developmental Neurorehabilitation 2013;16:67–72
   PubMed Web of Science ®Google Scholar
• Gallegos-Ayala G, Furdea A, Takano K, Ruf CA, Flor H, Birbaumer N. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology 2014;82:1930–1932
   PubMed Web of Science ®Google Scholar
• Mueller-Putz G, Scherer R, Brunner C, Leeb R, Pfurtscheller G. Better than random? A closer look on BCI results. International Journal of Bioelectromagnetism 2008;10:52–55
   Google Scholar
• Falk TH, Chan J, Duez P, Teachman G, Chau T. Augmentative communication based on realtime vocal cord vibration detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2010;18:159–163
   PubMed Web of Science ®Google Scholar
• ISS Inc. ISS Imagent: Functional brain imaging system using infrared photons. 2012
   Google Scholar
• Ogata H, Mukai T, Yagi T. A study on the frontal cortex in cognitive tasks using near-infrared spectroscopy. In: Proceedings of the 29th annual international conference of the IEEE EMBS; Lyon, France. 2007. pp 4731–4734
   Google Scholar
• Gao W, Wang H, Snyder MA, Li Y. The unique properties of the prefrontal cortex and mental illness. In: When things go wrong – Diseases and disorders of the human brain. Rijeka, Croatia InTech; 1990. pp 3–26
   Google Scholar
• Bauernfeind G, Leeb R, Wriessnegger SC, Pfurtscheller G. Development, set-up and first results for a one-channel near-infrared spectroscopy system. Biomed Tech 2008;53:36–43
   PubMed Web of Science ®Google Scholar
• Haeussinger FB, Heinzel S, Hahn T, Schecklmann M, Ehlis A-C, Fallgatter AJ. Simulation of near-infrared light absorption considering individual head and prefrontal cortex anatomy: Implications for optical neuroimaging. PLoS One 2011;6:e26377
   PubMed Web of Science ®Google Scholar
• Okada E, Firbank M, Schweiger M, Arridge SR, Cope M, Delpy DT. Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head. Applied Optics 1997;36:21–31
   PubMed Web of Science ®Google Scholar
• Power SD, Falk TH, Chau T. Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy. Journal of Neural Engineering 2010;7:026002
   PubMed Web of Science ®Google Scholar
• Power S, Kushki A, Chau T. Towards a system-paced near-infrared spectroscopy brain–computer interface: Differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state. Journal of Neural Engineering 2011;8:066004
   PubMed Web of Science ®Google Scholar
• Schudlo LC, Power SD, Chau T. Dynamic topographical pattern classification of multichannel prefrontal NIRS signals. Journal of Neural Engineering 2013;10:046018
   Web of Science ®Google Scholar
• Power SD, Kushki A, Chau T. Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS. PLoS One 2012;7:e37791
   PubMed Web of Science ®Google Scholar
• Weyand S, Schudlo L, Takehara-Nishiuchi K, Chau T. Usability and performance-informed selection of personalized mental tasks for an online near-infrared spectroscopy brain–computer interface. Neurophotonics 2015;2:025001
   PubMed Web of Science ®Google Scholar
• Weyand S, Takehara-Nishiuchi K, Chau T. Correlates of near-infrared spectroscopy brain–computer interface accuracy in a multi-class personalization framework. 2015
   Google Scholar
• Weyand S, Takehara-Nishiuchi K, Chau T. Weaning off mental tasks to achieve voluntary self-regulatory control of a near-infrared spectroscopy brain–computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2015;23:548–561
   PubMed Web of Science ®Google Scholar
• Weyand S, Takehara-Nishiuchi K, Chau T. Exploring methodological frameworks for a mental task-based near-infrared spectroscopy brain–computer interface. Journal of Neuroscience Methods 2015;254:36–45
   PubMed Web of Science ®Google Scholar
• Ang K, Yu J, Guan C. Extracting and selecting discriminative features from high density NIRS-based BCI for numerical cognition. In: The 2012 international joint conference on neural networks (IJCNN); Brisbane, Australia. Vol. 10. 2012. pp 1–6
   Google Scholar
• Herff C, Heger D, Putze F, Hennrich J, Fortmann O, Schultz T. Classification of mental tasks in the prefrontal cortex using fNIRS. In: Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Osaka, Japan. 2013. pp 2160–2163
   Google Scholar
• Utsugi K, Obata A, Sato H, Katsura T, Sagara K, Maki A, Koizumi H. Development of an optical brain–machine interface. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS; Lyon, France. 2007. pp 5338–5341
   Google Scholar
• Schudlo LC, Chau T. Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest. Journal of Neural Engineering 2014;11:016003
   PubMed Web of Science ®Google Scholar
• Faress A, Chau T. Towards a multimodal brain–computer interface: Combining fNIRS and fTCD measurements to enable higher classification accuracy. NeuroImage 2013;77:186–194
   PubMed Web of Science ®Google Scholar
• Naseer N, Hong K-S. Functional near-infrared spectroscopy based discrimination of mental counting and no-control state for development of a brain–computer interface. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Osaka, Japan. Vol. 2013. 2013. pp 1780–1783
   Google Scholar
• Tai K, Chau T. Single-trial classification of NIRS signals during emotional induction tasks: Towards a corporeal machine interface. Journal of Neuroengineering and Rehabilitation 2009;6:1–14
   PubMed Web of Science ®Google Scholar
• Koshino H, Minamoto T, Ikeda T, Osaka M, Otsuka Y, Osaka N. Anterior medial prefrontal cortex exhibits activation during task preparation but deactivation during task execution. PLoS One 2011;6(8):e22909
   PubMed Web of Science ®Google Scholar
• Izzetoglu K, Ayaz H, Merzagora A, Izzetoglu M, Shewokis PA, Bunce SC, Pourrezaei K, Rosen A, Onaral B. The evolution of field deployable fNIR spectroscopy from bench to clinical settings. Journal of Innovative Optical Health Sciences 2011;4:239–250
   Web of Science ®Google Scholar
• Cope M. The application of near infrared spectroscopy to non invasive monitoring of cerebral oxygenation in the newborn infant. London, UK: University of London; 1991. pp 2–305
   Google Scholar
• Duncan A, Meek JH, Clemence M, Elwell CE, Tyszczuk L, Cope M, Delpy DT. Optical pathlength measurements on adult head, calf and forearm and the head of the newborn infant using phase resolved optical spectroscopy. Physics in Medicine and Biology 1995;40:295–304
   PubMed Web of Science ®Google Scholar
• Yu L, Liu H. Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Proceedings of the Twentieth International Conference on Machine Learning. Vol. 2. 2003. p. 856–863
   Google Scholar
• Refaeilzadeh P, Tang L, Liu H. Cross-validation. Encyclopedia of Database Systems. Vol. 71. New York: Springer; 2009
   Google Scholar
• Pfurtscheller G, Allison BZ, Brunner C, Bauernfeind G, Solis-Escalante T, Scherer R, Zander TO, Mueller-Putz G, Neuper C, Birbaumer N. The hybrid BCI. Frontiers in Neuroscience 2010;4:Article 30
   Google Scholar
• Ahn M, Cho H, Ahn S, Jun SC. High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PLoS One 2013;8:e80886
   PubMed Web of Science ®Google Scholar
• Leung B, Brian JA, Chau T. Learning and mastery behaviours as risk factors to abandonment in a paediatric user of advanced single-switch access technology. Disability and Rehabilitation. Assistive Technology 2013;8:426–433
   PubMedGoogle Scholar
• Koegel RL, Mentis M. Motivation in childhood autism: Can they or won’t they? Journal of Child Psychology and Psychiatry, and Allied Disciplines 1985;26:185–191
   PubMed Web of Science ®Google Scholar
• Neary D, Snowden J., Mann DM. Cognitive change in motor neurone disease/amyotrophic lateral sclerosis (MND/ALS). Journal of the Neurological Sciences 2000;180(1–2):15–20
   PubMed Web of Science ®Google Scholar
• Lillo P, Hodges JR. Cognition and behaviour in motor neurone disease. Current Opinion in Neurology 2010;23:638–642
   PubMed Web of Science ®Google Scholar
• D’Angelo MG, Bresolin N. Cognitive impairment in neuromuscular disorders. Muscle & Nerve 2006;34:16–33
   PubMed Web of Science ®Google Scholar
• Quijano-Roy S, Martí-Carrera I, Makri S, Mayer M, Maugenre S, Richard P, Berard C, Viollet L, Leheup B, Guicheney P, et al. Brain MRI abnormalities in muscular dystrophy due to FKRP mutations. Brain and Development 2006;28:232–242
   PubMed Web of Science ®Google Scholar
• Albrecht T, Burleson B, Sarason I. Meaning and method in the study of communication and social support: An introduction. Communication Research 1992;19:149–153
   Web of Science ®Google Scholar
• McColl MA, Skinner H. Assessing inter- and intrapersonal resources: Social support and coping among adults with a disability. Disability & Rehabilitation 1995;17:24–34
   PubMed Web of Science ®Google Scholar
• Schulz R, Decker S. Long-term adjustment to physical disability: The role of social support, perceived control, and self-blame. Journal of Personality and Social Psychology 1985;48:1162–1172
   PubMed Web of Science ®Google Scholar
• Daffner KR, Mesulam MM, Scinto LF, Acar D, Calvo V, Faust R, Chabrerie A, Kennedy B, Holcomb P. The central role of the prefrontal cortex in directing attention to novel events. Brain: A Journal of Neurology 2000;123:927–939
   PubMed Web of Science ®Google Scholar
• Weierich MR, Wright CI, Negreira A, Dickerson BC, Barrett LF. Novelty as a dimension in the affective brain. NeuroImage 2010;49:2871–2878
   PubMed Web of Science ®Google Scholar
• Aluja A, Blanch A, Blanco E, Balada F. Affective modulation of the startle reflex and the Reinforcement Sensitivity Theory of personality: The role of sensitivity to reward. Physiology & Behavior 2015;138:332–339
   PubMed Web of Science ®Google Scholar
• Rogers RD, Owen M, Middleton HC, Williams EJ, Pickard JD, Sahakian BJ, Robbins TW. Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex. The Journal of Neuroscience 1999;19:9029–9038
   PubMed Web of Science ®Google Scholar
• Smith AB, Halari R, Giampetro V, Brammer M, Rubia K. Developmental effects of reward on sustained attention networks. NeuroImage 2011;56:1693–1704
   PubMed Web of Science ®Google Scholar
• Rahimi-Golkhandan S, Piek JP, Steenbergen B, Wilson PH. Hot executive function in children with Developmental Coordination Disorder: Evidence for heightened sensitivity to immediate reward. Cognitive Development 2014;32:23–37
   Web of Science ®Google Scholar
• Mak JN, Wolpaw JR. Clinical applications of brain–computer interfaces: Current state and future prospects. IEEE Reviews in Biomedical Engineering 2009;2:187–199
   PubMedGoogle Scholar
• Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kübler A. An auditory brain–computer interface (BCI). Journal of Neuroscience Methods 2008;167:43–50
   PubMed Web of Science ®Google Scholar
• Hammer EM, Halder S, Blankertzc B, Sannelli C, Dickhaus T, Kleihb S, Müller K-R, Kübler A. Psychological predictors of SMR-BCI performance. Biological Psychology 2012;89:80–86
   PubMed Web of Science ®Google Scholar