Examining sensory ability, feature matching and assessment-based adaptation for a brain–computer interface using the steady-state visually evoked potential

References

• Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002;113:767–791.
   PubMed Web of Science ®Google Scholar
• Brumberg JS, Pitt KM, Mantie-Kozlowski A, et al. Brain-computer interfaces for augmentative and alternative communication: a tutorial. Am J Speech-Lang Pat. 2018;1–12.
   PubMed Web of Science ®Google Scholar
• Beukelman D, Mirenda P. Augmentative and alternative communication: supporting children and adults with complex communication needs. 4th ed. Baltimore (MD): Paul H. Brookes Publishing Co; 2013.
   Google Scholar
• Higginbotham DJ, Shane H, Russell S, et al. Access to AAC: present, past, and future. Augment Altern Commun. 2007;23:243–257.
   PubMed Web of Science ®Google Scholar
• Fager S, Beukelmanm DR, Fried-Oken M, et al. Access interface strategies. Assist Technol. 2012;24:25–33.
   Web of Science ®Google Scholar
• Plum F, Posner JB. The diagnosis of stupor and coma. Contemp Neurol Ser. 1972;10:1–286.
   PubMedGoogle Scholar
• Bauer G, Gerstenbrand F, Rumpl E. Varieties of the locked-in syndrome. J Neurol. 1979;221:77–91.
   PubMed Web of Science ®Google Scholar
• Brumberg JS, Guenther FH. Development of speech prostheses: current status and recent advances. Expert Rev Med Devices. 2010;7:667–679.
   PubMed Web of Science ®Google Scholar
• Fried-Oken M, Mooney A, Peters B, et al. A clinical screening protocol for the RSVP Keyboard brain-computer interface. Disabil Rehabil Assist Technol. 2013;10:11–18.
   PubMed Web of Science ®Google Scholar
• Oken BS, Orhan U, Roark B, et al. Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome. Neurorehabil Neural Repair. 2014;28:387–394.
   PubMed Web of Science ®Google Scholar
• Kübler A, Holz EM, Sellers EW, et al. Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users. Arch Phys Med Rehabil. 2015;96:S27–S32.
   PubMed Web of Science ®Google Scholar
• Light J, McNaughton D. Putting people first: re-thinking the role of technology in augmentative and alternative communication intervention. Augment Altern Commun. 2013;29:299–309.
   PubMed Web of Science ®Google Scholar
• Gosnell J, Costello J, Shane H. Using a clinical approach to answer what communication apps should we use? Perspect Augment Altern Commun. 2011;20:87–96.
   Google Scholar
• Thistle JJ, Wilkinson KM. Building evidence-based practice in AAC display design for young children: current practices and future directions. Augment Altern Commun. 2015;31:124–136.
   PubMed Web of Science ®Google Scholar
• Sutter EE. The brain response interface: communication through visually-induced electrical brain responses. J Microcomput Appl. 1992;15:31–45.
   Google Scholar
• Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehab Eng. 2000;8:174–179.
   PubMedGoogle Scholar
• 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;11:035003.
   PubMed Web of Science ®Google Scholar
• Halder S, Rea M, Andreoni R, et al. An auditory oddball brain-computer interface for binary choices. Clin Neurophysiol. 2010;121:516–523.
   PubMed Web of Science ®Google Scholar
• Blankertz B, Dornhege G, Krauledat M, et al. The Berlin brain-computer interface: EEG-based communication without subject training. IEEE Trans Neural Syst Rehabil Eng. 2006;14:147–152.
   PubMed Web of Science ®Google Scholar
• Neuper C, Müller-Putz GR, Scherer R, et al. Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog Brain Res. 2006;159:393–409.
   PubMed Web of Science ®Google Scholar
• Brumberg JS, Burnison JD, Pitt KM. Using motor imagery to control brain-computer interfaces for communication. In: Schmorrow D, Fidopiastis C. (eds) Foundations of augmented cognition: neuroergonomics and operational neuroscience. Bern (Switzerland): Spring International Publishing; 2016. p. 14–25.
   Google Scholar
• Light J. Toward a definition of communicative competence for individuals using augmentative and alternative communication systems. Augment Altern Commun. 1989;5:137–144.
   Google Scholar
• Hill K, Kovacs T, Shin S. Critical issues using brain-computer interfaces for augmentative and alternative communication. Arch Phys Med Rehabil. 2015;96:S8–S15.
   PubMed Web of Science ®Google Scholar
• Middendorf M, McMillan G, Calhoun G, et al. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehab Eng. 2000;8:211–214.
   PubMedGoogle Scholar
• Cheng M, Gao X, Gao S, et al. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng. 2002;49:1181–1186.
   PubMed Web of Science ®Google Scholar
• Kelly SP, Lalor EC, Finucane C, et al. Visual spatial attention control in an independent brain-computer interface. IEEE Trans Biomed Eng. 2005;52:1588–1596.
   PubMed Web of Science ®Google Scholar
• Kelly SP, Lalor EC, Reilly RB, et al. Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Trans Neural Syst Rehabil Eng. 2005;13:172–178.
   PubMed Web of Science ®Google Scholar
• Allison BZ, McFarland DJ, Schalk G, et al. Towards an independent brain-computer interface using steady state visual evoked potentials. Clin Neurophysiol. 2008;119:399–408.
   PubMed Web of Science ®Google Scholar
• Zhu D, Bieger J, Garcia Molina G, et al. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci. 2010;2010:702357.
   Google Scholar
• Regan D. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. New York: Elsevier; 1989.
   Google Scholar
• Regan D. Steady-state evoked potentials. J Opt Soc Am. 1977;67:1475.
   PubMed Web of Science ®Google Scholar
• Müller-Putz GR, Scherer R, Brauneis C, et al. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng. 2005;2:123–130.
   PubMed Web of Science ®Google Scholar
• Lotte F, Congedo M, Lécuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng. 2007;4:R1–R13.
   PubMed Web of Science ®Google Scholar
• Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-Based BCIs. IEEE Trans Biomed Eng. 2007;54:1172–1176.
   PubMed Web of Science ®Google Scholar
• Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng. 2015;12:46008.
   PubMed Web of Science ®Google Scholar
• Brunner P, Joshi S, Briskin S, et al. Does the ‘P300’ speller depend on eye gaze? J Neural Eng. 2010;7:056013.
   PubMed Web of Science ®Google Scholar
• Müller MM, Andersen S, Trujillo NJ, et al. Feature-selective attention enhances color signals in early visual areas of the human brain. Proc Natl Acad Sci USA. 2006;103:14250–14254.
   PubMed Web of Science ®Google Scholar
• Walter S, Quigley C, Andersen SK, et al. Effects of overt and covert attention on the steady-state visual evoked potential. Neurosci Lett. 2012;519:37–41.
   PubMed Web of Science ®Google Scholar
• Lesenfants D, Partoune N, Soddu A, et al. Design of a novel covert SSVEP-based BCI. Proceedings of the 5th International Brain-Computer Interface Conference 2011; Sept 22–24; Graz, Austria. University of Technology Publishing House; 2011.
   Google Scholar
• Zhang D, Maye A, Gao X, et al. An independent brain–computer interface using covert non-spatial visual selective attention. J Neural Eng. 2010;7:016010.
   PubMed Web of Science ®Google Scholar
• Lesenfants D, Habbal D, Lugo Z, et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J Neural Eng. 2014;11:035002.
   PubMed Web of Science ®Google Scholar
• Jasper HH. The 10–20 electrode system of the International Federation. Electroencephalogr Clin Neurophysiol. 1958;10:371–375.
   Google Scholar
• Müller-Putz GR, Eder E, Wriessnegger SC, et al. Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI. J Neurosci Methods. 2008;168:174–181.
   PubMed Web of Science ®Google Scholar
• R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2015.
   Google Scholar
• Allison B, Lüth T, Valbuena D, et al. BCI demographics: how many (and what kinds of) people can use an SSVEP BCI? IEEE Trans Neural Syst Rehabil Eng. 2010;18:107–116.
   PubMed Web of Science ®Google Scholar
• Ahn M, Jun SC. Performance variation in motor imagery brain-computer interface: a brief review. J Neurosci Methods. 2015;243:103–110.
   PubMed Web of Science ®Google Scholar
• Peters B, Mooney A, Oken B, et al. Soliciting BCI user experience feedback from people with severe speech and physical impairments. Brain Comput Interfaces. 2016;3:47–58.
   Web of Science ®Google Scholar
• Nijboer F, Birbaumer N, Kübler 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;4:1–13.
   PubMed Web of Science ®Google Scholar
• Geronimo A, Stephens HE, Schiff SJ, et al. Acceptance of brain-computer interfaces in amyotrophic lateral sclerosis. Amyotroph Lat Scl Fr. 2015;16:258–264.
   PubMed Web of Science ®Google Scholar
• Riccio A, Simione L, Schettini F, et al. Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis. Front Hum Neurosci. 2013;7:732.
   PubMed Web of Science ®Google Scholar
• Sprague SA, McBee MT, Sellers EW. The effects of working memory on brain-computer interface performance. Clin Neurophysiol. 2016;127:1331–1341.
   PubMed Web of Science ®Google Scholar
• Geronimo AM, Jin Q, Schiff SJ, et al. Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis. J Neural Eng. 2016;13:026002.
   PubMed Web of Science ®Google Scholar