Applying Implementation Science to Support Active Collaboration in Noninvasive Brain–Computer Interface Development and Translation for Augmentative and Alternative Communication

References

• Allison, B. (2009). The I of BCIs: Next generation interfaces for brain–computer interface systems that adapt to individual users.In J. A. Jacko (Ed.), Human–computer interaction: Novel interaction methods and techniques (Vol. 2, pp. 558–568). Springer. https://doi.org/10.1007/978-3-642-02577-8_61
  Google Scholar
• Andresen, E., Fried-Oken, M., Peters, B., & Patrick, D. (2016). Initial constructs for patient-centered outcome measures to evaluate brain–computer interfaces.Disability and Rehabilitation: Assistive Technology, 11(7), 548–557. https://doi.org/10.3109/17483107.2015.1027298
  CrossrefMedlineGoogle Scholar
• Bauer, M. S., Damschroder, L., Hagedorn, H., Smith, J., & Kilbourne, A. M. (2015). An introduction to implementation science for the non-specialist.BMC Psychology, 3(1), 32–12. https://doi.org/10.1186/s40359-015-0089-9
  CrossrefMedlineGoogle Scholar
• Beukelman, D. R., & Light, J. C. (2020). Augmentative and alternative communication: Supporting children and adults with complex communication needs (5th ed.). Brookes.
  Google Scholar
• Blain-Moraes, S., Schaff, R., Gruis, K. L., Huggins, J. E., & Wren, P. A. (2012). Barriers to and mediators of brain–computer interface user acceptance: Focus group findings.Ergonomics, 55(5), 516–525. https://doi.org/10.1080/00140139.2012.661082
  CrossrefMedlineGoogle Scholar
• Broomfield, K., Harrop, D., Judge, S., Jones, G., & Sage, K. (2019). Appraising the quality of tools used to record patient-reported outcomes in users of augmentative and alternative communication (AAC): A systematic review.Quality of Life Research, 28, 2669–2683. https://doi.org/10.1007/s11136-019-02228-3
  CrossrefMedlineGoogle Scholar
• Brumberg, J. S., Nguyen, A., Pitt, K. M., & Lorenz, S. D. (2018). Examining sensory ability, feature matching and assessment-based adaptation for a brain–computer interface using the steady-state visually evoked potential.Disability and Rehabilitation: Assistive Technology, 14(3), 241–249. https://doi.org/10.1080/17483107.2018.1428369
  CrossrefMedlineGoogle Scholar
• Brumberg, J. S., Pitt, K. M., Mantie-Kozlowski, A., & Burnison, J. D. (2018). Brain–computer interfaces for augmentative and alternative communication: A tutorial.American Journal of Speech-Language Pathology, 27(1), 1–12. https://doi.org/10.1044/2017_ajslp-16-0244
  ASHAWireGoogle Scholar
• Burde, W., & Blankertz, B. (2006). Is the locus of control of reinforcement a predictor of brain–computer interface performance?.G. Müller-Putz, C. Brunner, R. Leeb, R. Scherer, A. Schlögl, S. Wriessnegger, & G. Pfurtscheller (Eds.), Proceedings of the 3rd international brain–computer interface workshop and training course (pp. 108–109). Verlag der Technischen Universität Graz.
  Google Scholar
• Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017). Heading for new shores! Overcoming pitfalls in BCI design.Brain–Computer Interfaces, 4(1–2), 60–73. https://doi.org/10.1080/2326263x.2016.1263916
  CrossrefMedlineGoogle Scholar
• Damschroder, L., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science.Implementation Science, 4(1), 50. https://doi.org/10.1186/1748-5908-4-50
  CrossrefMedlineGoogle Scholar
• Damschroder, L., Hall, C., Gillon, L., Reardon, C., Kelley, C., Sparks, J., & Lowery, J. (2015). The Consolidated Framework for Implementation Research (CFIR): Progress to date, tools and resources, and plans for the future.Implementation Science, 10(S1), A12. https://doi.org/10.1186/1748-5908-10-S1-A12
  CrossrefMedlineGoogle Scholar
• da Silva-Sauer, L., Torre-Luque, A., Silva, J. S. C., & Fernández-Calvo, B. (2019). New perspectives for cognitive rehabilitation: Could brain–computer interface systems benefit people with dementia?.Psychology & Neuroscience, 12(1), 25–37. https://doi.org/10.1037/pne0000154
  CrossrefGoogle Scholar
• Debener, S., Minow, F., Emkes, R., Gandras, K., & de Vos, M. (2012). How about taking a low-cost, small, and wireless EEG for a walk?.Psychophysiology, 49(11), 1617–1621. https://doi.org/10.1111/j.1469-8986.2012.01471.x
  CrossrefMedlineGoogle Scholar
• Donchin, E., Spencer, K. M., & Wijesinghe, R. (2000). The mental prosthesis: Assessing the speed of a P300-based brain–computer interface.IEEE Transactions on Rehabilitation Engineering, 8(2), 174–179. https://doi.org/10.1109/86.847808
  CrossrefMedlineGoogle Scholar
• Douglas, N. F., & Burshnic, V. L. (2018). Implementation science: Tackling the research to practice gap in communication sciences and disorders.Perspectives of the ASHA Special Interest Groups, 4(1), 3–7. https://doi.org/10.1044/2018_PERS-ST-2018-0000
  ASHAWireGoogle Scholar
• Douglas, N. F., Campbell, W. N., & Hinckley, J. J. (2015). Implementation science: Buzzword or game changer?.Journal of Speech, Language, and Hearing Research, 58(6), S1827–S1836. https://doi.org/10.1044/2015_JSLHR-L-15-0302
  ASHAWireGoogle Scholar
• Eccles, M. P., & Mittman, B. S. (2006). Welcome to implementation science.Implementation Science, 1, 1. https://doi.org/10.1186/1748-5908-1-1
  CrossrefGoogle Scholar
• Fager, S. K., Fried-Oken, M., Jakobs, T., & Beukelman, D. R. (2019). New and emerging access technologies for adults with complex communication needs and severe motor impairments: State of the science.Augmentative & Alternative Communication, 35(1), 13–25. https://doi.org/10.1080/07434618.2018.1556730
  CrossrefMedlineGoogle Scholar
• Frambach, R. T., & Schillewaert, N. (2002). Organizational innovation adoption: A multi-level framework of determinants and opportunities for future research.Journal of Business Research, 55(2), 163–176. https://doi.org/10.1016/S0148-2963(00)00152-1
  CrossrefGoogle Scholar
• Fried-Oken, M., Mooney, A., Peters, B., & Oken, B. (2013). A clinical screening protocol for the RSVP Keyboard brain–computer interface.Disability and Rehabilitation: Assistive Technology, 10(1), 11–18. https://doi.org/10.3109/17483107.2013.836684
  CrossrefMedlineGoogle Scholar
• Geronimo, A., & Simmons, Z. (2020). TeleBCI: Remote user training, monitoring, and communication with an evoked-potential brain–computer interface.Brain-Computer Interfaces, 7(3–4), 57–69. https://doi.org/10.1080/2326263x.2020.1848134
  CrossrefMedlineGoogle Scholar
• Geronimo, A., Stephens, H. E., Schiff, S. J., & Simmons, Z. (2015). Acceptance of brain–computer interfaces in amyotrophic lateral sclerosis.Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 16(3–4), 258–264. https://doi.org/10.3109/21678421.2014.969275
  CrossrefMedlineGoogle Scholar
• Glasgow, R. E., Vinson, C., Chambers, D., Khoury, M. J., Kaplan, R. M., & Hunter, C. (2012). National Institutes of Health approaches to dissemination and implementation science: Current and future directions.American Journal of Public Health, 102(7), 1274–1281. https://doi.org/10.2105/AJPH.2012.300755
  CrossrefMedlineGoogle Scholar
• Gosmanova, K. A., Carmack, C. S., Goldberg, D., Fitzpatrick, K., Zoltan, B., Zeitlin, D. M., Wolpaw, J. R., Maehle, O. A., Borge, A., & Vaughan, T. M. (2017). EEG-based brain–computer interface access to Tobii Dynavox Communicator 5 [Paper presentation]. Rehabilitation Engineering and Assistive Technology Society of North America. Retrieved April 22, 2021, from https://www.resna.org/sites/default/files/conference/2017/cac/Gosmanova.html
  Google Scholar
• Guger, C., Krausz, G., Allison, B. Z., & Edlinger, G. (2012). Comparison of dry and gel based electrodes for P300 brain–computer interfaces.Frontiers in Neuroscience, 6, 60. https://doi.org/10.3389/fnins.2012.00060
  CrossrefMedlineGoogle Scholar
• Higginbotham, D. J., Shane, H., Russell, S., & Caves, K. (2007). Access to AAC: Present, past, and future.Augmentative and Alternative Communication, 23(3), 243–257. https://doi.org/10.1080/07434610701571058
  CrossrefMedlineGoogle Scholar
• Hill, K., Huggins, J., & Woodworth, C. (2021). Interprofessional practitioners' opinions on features and services for an augmentative and alternative communication brain-computer interface device.PM&R, 13(10), 1111–1121. https://doi.org/10.1002/pmrj.12525
  CrossrefMedlineGoogle Scholar
• Hill, K., Kovacs, T., & Shin, S. (2015). Critical issues using brain–computer interfaces for augmentative and alternative communication.Archives of Physical Medicine and Rehabilitation, 96(3), S8–S15. https://doi.org/10.1016/j.apmr.2014.01.034
  CrossrefMedlineGoogle Scholar
• Huggins, J. E., Guger, C., Ziat, M., Zander, T. O., Taylor, D., Tangermann, M., Soria-Frisch, A., Simeral, J., Scherer, R., Rupp, R., Ruffini, G., Robinson, D. K. R., Ramsey, N. F., Nijholt, A., Müller-Putz, G., McFarland, D. J., Mattia, D., Lance, B. J., Kindermans, P.-J., … Aernoutse, E. J. (2017). Workshops of the sixth international brain–computer interface meeting: Brain–computer interfaces past, present, and future.Brain-Computer Interfaces, 4(1–2), 3–36. https://doi.org/10.1080/2326263X.2016.1275488
  CrossrefMedlineGoogle Scholar
• Huggins, J. E., & Kovacs, T. (2018). Brain–computer interfaces for augmentative and alternative communication: Separating the reality from the hype.Perspectives of the ASHA Special Interest Groups, 3(12), 13–23. https://doi.org/10.1044/persp3.sig12.13
  ASHAWireGoogle Scholar
• Huggins, J. E., Wren, P., & Gruis, K. (2011). What would brain–computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis.Amyotrophic Lateral Sclerosis, 12(5), 318–324. https://doi.org/10.3109/17482968.2011.572978
  CrossrefMedlineGoogle Scholar
• Igbaria, M. (1993). User acceptance of microcomputer technology: An empirical test.Omega, 21(1), 73–90. https://doi.org/10.1016/0305-0483(93)90040-R
  CrossrefGoogle Scholar
• Ikegami, S., Takano, K., Saeki, N., & Kansaku, K. (2011). Operation of a P300-based brain–computer interface by individuals with cervical spinal cord injury.Clinical Neurophysiology, 122(5), 991–996. https://doi.org/10.1016/j.clinph.2010.08.021
  CrossrefMedlineGoogle Scholar
• Johnson, J. M., Inglebret, E., Jones, C., & Ray, J. (2006). Perspectives of speech language pathologists regarding success versus abandonment of AAC.Augmentative and Alternative Communication, 22(2), 85–99. https://doi.org/10.1080/07434610500483588
  CrossrefMedlineGoogle Scholar
• Käthner, I., Kübler, A., & Halder, S. (2015). Comparison of eye tracking, electrooculography and an auditory brain–computer interface for binary communication: A case study with a participant in the locked-in state.Journal of NeuroEngineering and Rehabilitation, 12(1), 76. https://doi.org/10.1186/s12984-015-0071-z
  CrossrefMedlineGoogle Scholar
• Kent-Walsh, J., & Binger, C. (2018). Methodological advances, opportunities, and challenges in AAC research.Augmentative and Alternative Communication, 34(2), 93–103. https://doi.org/10.1080/07434618.2018.1456560
  CrossrefMedlineGoogle Scholar
• Kilbourne, A. M., Glasgow, R. E., & Chambers, D. A. (2020). What can implementation science do for you? Key success stories from the field.Journal of General Internal Medicine, 35, 783–787. https://doi.org/10.1007/s11606-020-06174-6
  CrossrefMedlineGoogle Scholar
• Kinney-Lang, E., Kelly, D., Floreani, E. D., Jadavji, Z., Rowley, D., Zewdie, E. T., Anaraki, J. R., Bahari, H., Beckers, K., Castelane, K., Crawford, L., House, S., Rauh, C. A., Michaud, A., Mussi, M., Silver, J., Tuck, C., Adams, K., Andersen, J., … Kirton, A. (2020). Advancing brain–computer interface applications for severely disabled children through a multidisciplinary national network: Summary of the inaugural pediatric BCI Canada meeting.Frontiers in Human Neuroscience, 14, 593883. https://doi.org/10.3389/fnhum.2020.593883
  CrossrefMedlineGoogle Scholar
• Kleih, S. C., Gottschalt, L., Teichlein, E., & Weilbach, F. X. (2016). Toward a P300 based brain–computer interface for aphasia rehabilitation after stroke: Presentation of theoretical considerations and a pilot feasibility study.Frontiers in Human Neuroscience, 10, 547. https://doi.org/10.3389/fnhum.201
  CrossrefMedlineGoogle Scholar
• Kögel, J., Jox, R. J., & Friedrich, O. (2020). What is it like to use a BCI?–Insights from an interview study with brain–computer interface users.BMC Medical Ethics, 21, 2. https://doi.org/10.1186/s12910-019-0442-2
  CrossrefMedlineGoogle Scholar
• Kübler, A., Holz, E., Riccio, A., Zickler, C., Kaufmann, T., Kleih, S., Staiger-Sälzer, P., Desideri, L., Hoogerwerf, E., & Mattia, D. (2014). The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications.PLOS ONE, 9(12), Article e112392. https://doi.org/10.1371/journal.pone.0112392
  CrossrefGoogle Scholar
• Light, J., & McNaughton, D. (2015). Designing AAC research and intervention to improve outcomes for individuals with complex communication needs.Augmentative and Alternative Communication, 31(2), 85–96. https://doi.org/10.3109/07434618.2015.1036458
  CrossrefMedlineGoogle Scholar
• Lorenz, R., Pascual, J., Blankertz, B., & Vidaurre, C. (2014). Towards a holistic assessment of the user experience with hybrid BCIs.Journal of Neural Engineering, 11(3), Article 035007. https://doi.org/10.1088/1741-2560/11/3/035007
  CrossrefMedlineGoogle Scholar
• Lotte, F., Larrue, F., & Mühl, C. (2013). Flaws in current human training protocols for spontaneous brain–computer interfaces: Lessons learned from instructional design.Frontiers in Human Neuroscience, 7, 568. https://doi.org/10.3389/fnhum.2013.00568
  CrossrefMedlineGoogle Scholar
• Mayaud, L., Congedo, M., Van Laghenhove, A., Orlikowski, D., Figère, M., Azabou, E., & Cheliout-Heraut, F. (2013). A comparison of recording modalities of P300 event-related potentials (ERP) for brain–computer interface (BCI) paradigm.Neurophysiologie Clinique/Clinical Neurophysiology, 43(4), 217–227. https://doi.org/10.1016/j.neucli.2013.06.002
  CrossrefMedlineGoogle Scholar
• Mendel, P., Meredith, L. S., Schoenbaum, M., Sherbourne, C. D., & Wells, K. B. (2008). Interventions in organizational and community context: A framework for building evidence on dissemination and implementation in health services research.Administration and Policy in Mental Health and Mental Health Services Research, 35(1–2), 21–37. https://doi.org/10.1007/s10488-007-0144-9
  CrossrefMedlineGoogle Scholar
• Meyers, D. C., Durlak, J. A., & Wandersman, A. (2012). The quality implementation framework: A synthesis of critical steps in the implementation process.American Journal of Community Psychology, 50(3–4), 462–480. https://doi.org/10.1007/s10464-012-9522-x
  CrossrefMedlineGoogle Scholar
• Moir, T. (2018). Why is implementation science important for intervention design and evaluation within educational settings?, Frontiers in Education, 3, 61. https://doi.org/10.3389/feduc.2018.00061
  Google Scholar
• Nilsen, P. (2015). Making sense of implementation theories, models and frameworks.Implementation Science, 10, 53. https://doi.org/10.1186/s13012-015-0242-0
  CrossrefMedlineGoogle Scholar
• Nilsen, P., & Bernhardsson, S. (2019). Context matters in implementation science: A scoping review of determinant frameworks that describe contextual determinants for implementation outcomes.BMC Health Services Research, 19(1), 189. https://doi.org/10.1186/s12913-019-4015-3
  CrossrefMedlineGoogle Scholar
• Ogden, T., & Fixsen, D. L. (2014). Implementation Science.Zeitschrift für Psychologie, 222(1), 4–11. https://doi.org/10.1027/2151-2604/a000160
  CrossrefGoogle Scholar
• Olswang, L., & Prelock, P. (2015). Bridging the gap between research and practice: Implementation science.Journal of Speech, Language, and Hearing Research, 58(6), S1818–S1826. https://doi.org/10.1044/2015_JSLHR-L-14-0305
  ASHAWireGoogle Scholar
• Pasqualotto, E., Matuz, T., Federici, S., Ruf, C. A., Bartl, M., Olivetti Belardinelli, M., Birbaumer, N., & Halder, S. (2015). Usability and workload of access technology for people with severe motor impairment: A comparison of brain–computer interfacing and eye tracking.Neurorehabilitation and Neural Repair, 29(10), 950–957. https://doi.org/10.1177/1545968315575611
  CrossrefMedlineGoogle Scholar
• Peters, B., Bieker, G., Heckman, S. M., Huggins, J. E., Wolf, C., Zeitlin, D., & Fried-Oken, M. (2015). Brain–computer interface users speak up: The virtual users' forum at the 2013 international brain–computer interface meeting.Archives of Physical Medicine and Rehabilitation, 96(3), S33–S37. https://doi.org/10.1016/j.apmr.2014.03.037
  CrossrefMedlineGoogle Scholar
• Peters, B., Mooney, A., Oken, B., & Fried-Oken, M. (2016). Soliciting BCI user experience feedback from people with severe speech and physical impairments.Brain-Computer Interfaces, 3(1), 47–58. https://doi.org/10.1080/2326263x.2015.1138056
  CrossrefMedlineGoogle Scholar
• Peters, D. H., Adam, T., Alonge, O., Agyepong, I. A., & Tran, N. (2013). Implementation research: What it is and how to do it.BMJ, 347, 731–736. https://doi.org/10.1136/bmj.f6753
  Google Scholar
• Pitt, K. M., & Brumberg, J. S. (2018a). A screening protocol incorporating brain–computer interface feature matching considerations for augmentative and alternative communication.Assistive Technology, 32(3), 161–172. https://doi.org/10.1080/10400435.2018.1512175
  CrossrefMedlineGoogle Scholar
• Pitt, K. M., & Brumberg, J. S. (2018b). Guidelines for feature matching assessment of brain–computer interfaces for augmentative and alternative communication.American Journal of Speech-Language Pathology, 27(3), 950–964. https://doi.org/10.1044/2018_ajslp-17-0135
  ASHAWireGoogle Scholar
• Pitt, K. M., & Brumberg, J. S. (2021a). Evaluating person-centered factors associated with brain–computer interface access to a commercial augmentative and alternative communication paradigm.Assistive Technology, 1–10. https://doi.org/10.1080/10400435.2021.1872737
  Google Scholar
• Pitt, K. M., & Brumberg, J. S. (2021b). Evaluating the perspectives of those with severe physical impairments while learning BCI control of a commercial augmentative and alternative communication paradigm.Assistive Technology, 1–9. https://doi.org/10.1080/10400435.2021.1949405
  Google Scholar
• Pitt, K. M., Brumberg, J. S., Burnison, J. D., Mehta, J., & Kidwai, J. (2019). Behind the scenes of noninvasive brain–computer interfaces: A review of electroencephalography signals, how they are recorded, and why they matter.Perspectives of the ASHA Special Interests Groups, 4(6), 1622–1636. https://doi.org/10.1044/2019_PERS-19-00059
  ASHAWireGoogle Scholar
• Pitt, K. M., Brumberg, J. S., & Pitt, A. R. (2019). Evaluating person-centered factors associated with brain–computer interface access to a commercial augmentative and alternative communication paradigm.Assistive Technology Outcomes and Benefits, 13, 1–10. https://doi.org/10.1080/10400435.2021.1872737
  MedlineGoogle Scholar
• Pouplin, S., Roche, N., Vaugier, I., Cabanilles, S., Hugeron, C., & Bensmail, D. (2016). Text input speed in persons with cervical spinal cord injury.Spinal Cord, 54(2), 158–162. https://doi.org/10.1038/sc.2015.147
  CrossrefMedlineGoogle Scholar
• Proctor, E., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G., Bunger, A., Griffey, R., & Hensley, M. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda.Administration and Policy in Mental Health, 38(2), 65–76. https://doi.org/10.1007/s10488-010-0319-7
  CrossrefMedlineGoogle Scholar
• Rosenberg, R. N. (2003). Translating biomedical research to the bedside: A national crisis and a call to action.JAMA, 289(10), 1305–1306. https://doi.org/10.1001/jama.289.10.1305
  CrossrefMedlineGoogle Scholar
• Rosenberg, S., & Beukelman, D. (1987). The Participation Model.In C. A. Coston (Ed.), Proceedings of the national planners conference on assistive device service delivery (pp. 159–161). The Association for the Advancement of Rehabilitation Technology.
  Google Scholar
• Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khondaker, A. H., & Baumert, M. (2021). Progress in brain computer interface: Challenges and opportunities.Frontiers in Systems Neuroscience, 15, 578875. https://doi.org/10.3389/fnsys.2021.578875
  CrossrefMedlineGoogle Scholar
• Scherer, R., Billinger, M., Wagner, J., Schwarz, A., Hettich, D. T., Bolinger, E., Garcia, M. L., Navarro, J., & Müller-Putz, G. (2015). Thought-based row-column scanning communication board for individuals with cerebral palsy.Annals of Physical and Rehabilitation Medicine, 58(1), 14–22. https://doi.org/10.1016/j.rehab.2014.11.005
  CrossrefMedlineGoogle Scholar
• Shahriari, Y., Vaughan, T. M., McCane, L. M., Allison, B. Z., Wolpaw, J. R., & Krusienski, D. J. (2019). An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data.Journal of Neural Engineering, 16(5), Article 056031. https://doi.org/10.1088/1741-2552/ab22ea
  CrossrefGoogle Scholar
• Shenoy, P., Krauledat, M., Blankertz, B., Rao, R., & Müller, K. (2006). Towards adaptive classification for BCI.Journal of Neural Engineering, 3(1), R13–R23. https://doi.org/10.1088/1741-2560/3/1/r02
  CrossrefMedlineGoogle Scholar
• Stevens, E. R., Shelley, D., & Boden-Albala, B. (2020). Perceptions of barriers and facilitators to engaging in implementation science: A qualitative study.Public Health, 185, 318–323. https://doi.org/10.1016/j.puhe.2020.06.016
  CrossrefMedlineGoogle Scholar
• Talukdar, U., Hazarika, S. M., & Gan, J. Q. (2019). Motor imagery and mental fatigue: Inter-relationship and EEG based estimation.Journal of Computational Neuroscience, 46, 55–76. https://doi.org/10.1007/s10827-018-0701-0
  CrossrefMedlineGoogle Scholar
• Taylor, M. J., McNicholas, C., Nicolay, C., Darzi, A., Bell, D., & Reed, J. E. (2014). Systematic review of the application of the plan–do–study–act method to improve quality in healthcare.BMJ Quality & Safety, 23(4), 290–298. https://doi.org/10.1136/bmjqs-2013-001862
  CrossrefMedlineGoogle Scholar
• Thompson, D. E., Quitadamo, L. R., Mainardi, L., Laghari, K., Gao, S., Kindermans, P.-J., Simeral, J. D., Fazel-Rezai, R., Matteucci, M., Falk, T. H., Bianchi, L., Chestek, C. A., & Huggins, J. E. (2014). Performance measurement for brain–computer or brain–machine interfaces: A tutorial.Journal of Neural Engineering, 11(3), Article 035001. https://doi.org/10.1088/1741-2560/11/3/035001
  CrossrefGoogle Scholar
• Tohidast, S., Mansuri, B., Bagheri, R., & Azimi, H. (2020). Provision of speech-language pathology services for the treatment of speech and language disorders in children during the COVID-19 pandemic: Problems, concerns, and solutions.International Journal of Pediatric Otorhinolaryngology, 138, 110262. https://doi.org/10.1016/j.ijporl.2020.110262
  CrossrefMedlineGoogle Scholar
• Warren, B., & Randolph, A. B. (2019). Facebrain: A P300 BCI to Facebook.In F. D. Davis, R. Riedl, J. von Brocke, P.-M. Léger, & A. B. Randolph (Eds.), Information systems and neuroscience: NeuroIS Retreat 2018 (pp. 119–124). Springer. https://doi.org/10.1007/978-3-030-01087-4_14
  Google Scholar
• Wolpaw, J. R., Bedlack, R. S., Reda, D. J., Ringer, R. J., Banks, P. G., Vaughan, T. M., Heckman, S. M., McCane, L. M., Carmack, C. S., Winden, S., McFarland, D. J., Sellers, E. W., Shi, H., Paine, T., Higgins, D. S., Lo, A. C., Patwa, H. S., Hill, K. J., Huang, G. D., & Ruff, R. L. (2018). Independent home use of a brain–computer interface by people with amyotrophic lateral sclerosis.Neurology, 91(3), e258–e267. https://doi.org/10.1212/wnl.0000000000005812
  CrossrefMedlineGoogle Scholar
• World Health Organization. (2002). Towards a common language for functioning, disability and health: ICF. https://www.who.it/classifications/icf/icfbeginnersguide.pdf
  Google Scholar
• Yorkston, K., & Baylor, C. (2019). Patient-reported outcomes measures: An introduction for clinicians.Perspectives of the ASHA Special Interest Groups, 4(1), 8–15. https://doi.org/10.1044/2018_PERS-ST-2018-0001
  ASHAWireGoogle Scholar
• Zhang, J., Jadavji, Z., Zewdie, E., & Kirton, A. (2019). Evaluating if children can use simple brain computer interfaces.Frontiers in Human Neuroscience, 13, 24. https://doi.org/10.3389/fnhum.2019.00024
  CrossrefMedlineGoogle Scholar