Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease

References

• Akcakaya M, Peters B, Moghadamfalahi M, et al. Noninvasive brain–computer interfaces for augmentative and alternative communication. IEEE Rev Biomed Eng. 2014;7:31–49.
   PubMedGoogle Scholar
• Koch Fager S, Fried-Oken M, Jakobs T, et al. New and emerging access technologies for adults with complex communication needs and severe motor impairments: state of the science. Augment Altern Commun. 2019;35(1):13–25.
   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 Pathol. 2018;27(1):1–12.
   PubMed Web of Science ®Google Scholar
• Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–791.
   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(4):387–394.
   PubMed Web of Science ®Google Scholar
• Speier W, Arnold C, Pouratian N. Integrating language models into classifiers for BCI communication: a review. J Neural Eng. 2016;13(3):031002.
   PubMed Web of Science ®Google Scholar
• Newell A, Langer S, Hickey M. The role of natural language processing in alternative and augmentative communication. Nat Lang Eng. 1998;4(1):1–16.
   Google Scholar
• Vertanen K, Kristensson PO. The imagination of crowds: conversational AAC language modeling using crowdsourcing and large data sources. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing; 2011. p. 700–711.
   Google Scholar
• Higginbotham DJ, Lesher GW, Moulton BJ, et al. The application of natural language processing to augmentative and alternative communication. Assist Technol. 2011;24(1):14–24.
   PubMed Web of Science ®Google Scholar
• Waller A, McCoy K, Roark B, Fried-Oken M, McGregor, A. Natural language processing and AAC: current advances at the interface between technology and communication. Proceedings of International Society for Augmentative and Alternative Communication. Pittsburgh, PA; 2012.
   Google Scholar
• Wandmacher T, Antoine J-Y. Training language models without appropriate language resources: experiments with an AAC system for disabled people. Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC). 2006;:1842–1845.
   Google Scholar
• ISO 9241-210:2019. Ergonomics of human-system interaction – Part 210: human-centred design for interactive systems [Internet] [cited 2022 Jan 3]. Available from: https://webstore.ansi.org/standards/iso/iso92412102019?gclid=Cj0KCQiA2sqOBhCGARIsAPuPK0h2z2LFb9xgTdUIkyRIEtdEZzpklCX4DRx9EKmgaYEXZ55jgcKnvMUaAtQbEALw_wcB.
   Google Scholar
• Branco MP, Pels EG, Sars RH, et al. Brain-Computer interfaces for communication: preferences of individuals with locked-in syndrome. Neurorehabil Neural Repair. 2021;35(3):267–279.
   PubMed Web of Science ®Google Scholar
• Kübler 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;9(12):e112392.
   PubMed Web of Science ®Google Scholar
• Blabe CH, Gilja V, Chestek CA, et al. Assessment of brain–machine interfaces from the perspective of people with paralysis. J Neural Eng. 2015;12(4):043002.
   PubMed Web of Science ®Google Scholar
• Blain-Moraes S, Schaff R, Gruis KL, et al. Barriers to and mediators of brain–computer interface user acceptance: focus group findings. Ergonomics. 2012;55(5):516–525.
   PubMed Web of Science ®Google Scholar
• Kageyama Y, He X, Shimokawa T, et al. Nationwide survey of 780 Japanese patients with amyotrophic lateral sclerosis: their status and expectations from brain–machine interfaces. J Neurol. 2020;267(10):2932–2940.
   PubMed Web of Science ®Google Scholar
• Liberati G, Pizzimenti A, Simione L, et al. Developing brain-computer interfaces from a user-centered perspective: assessing the needs of persons with amyotrophic lateral sclerosis, caregivers, and professionals. Appl Ergon. 2015;50:139–146.
   PubMed Web of Science ®Google Scholar
• Peters B, Bieker G, Heckman SM, et al. Brain-computer interface users speak up: the virtual users’ forum at the 2013 international brain-computer interface meeting. Arch Phys Med Rehabil. 2015;96(3 Suppl):S33–S37.
   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(1):47–58.
   Web of Science ®Google Scholar
• Schicktanz S, Amelung T, Rieger JW. Qualitative assessment of patients’ attitudes and expectations toward BCIs and implications for future technology development. Front Syst Neurosci. 2015;9:64.
   PubMed Web of Science ®Google Scholar
• van de Laar B, Gürkök H, Bos DP-O, et al. Brain–computer interfaces and user experience evaluation. Practical brain-computer interfaces. Springer; Berlin, Heidelberg. 2012. p. 223–237.
   Google Scholar
• Klein E, Brown T, Sample M, et al. Engineering the brain: ethical issues and the introduction of neural devices. Hastings Cent Rep. 2015;45(6):26–35.
   PubMed Web of Science ®Google Scholar
• Burwell S, Sample M, Racine E. Ethical aspects of brain computer interfaces: a scoping review. BMC Med Ethics. 2017;18(1):1–11.
   PubMedGoogle Scholar
• Glannon W. 2021. Neural prosthetics: neuroscientific and philosophical aspects of changing the brain. Oxford, UK: Oxford University Press.
   Google Scholar
• Sullivan LS, Klein E, Brown T, et al. Keeping disability in mind: a case study in implantable brain–computer interface research. Sci Eng Ethics. 2018;24(2):479–504.
   PubMed Web of Science ®Google Scholar
• Versalovic E, Diamond M, Klein E. “Re-identifying yourself”: a qualitative study of veteran views on implantable BCI for mobility and communication in ALS. Disabil Rehabil Assist Technol. 2020;17(7):1–8.
   Web of Science ®Google Scholar
• Vlek RJ, Steines D, Szibbo D, et al. Ethical issues in brain–computer interface research, development, and dissemination. J Neurol Phys Ther. 2012;36(2):94–99.
   PubMed Web of Science ®Google Scholar
• Hillel AD, Miller RM, Yorkston K, et al. Amyotrophic lateral sclerosis severity scale. Neuroepidemiology. 1989;8(3):142–150.
   PubMed Web of Science ®Google Scholar
• Cedarbaum JM, Stambler N, Malta E, et al. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. J Neurol Sci. 1999;169(1–2):13–21.
   Google Scholar
• Brandt J, Spencer M, Folstein M. The telephone interview for cognitive status. Neuropsychiatry Neuropsychol Behav Neurol. 1988;1:111–117.
   Google Scholar
• Charmaz K. Constructing grounded theory: a practical guide through qualitative analysis. Los Angeles, CA: Sage; 2006.
   Google Scholar
• Strauss A, Corbin JM. Grounded theory in practice. Lost Angeles, CA: Sage; 1997.
   Google Scholar
• Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–1288.
   PubMed Web of Science ®Google Scholar
• Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107–115.
   PubMed Web of Science ®Google Scholar
• Corbin J, Strauss A. Strategies for qualitative data analysis. Basics Qual Res. 2008;3(10.4135):9781452230153.
   Google Scholar
• Blackstone SW, Williams MB, Joyce M. Future AAC technology needs: consumer perspectives. Assist Technol. 2002;14(1):3–16.
   PubMedGoogle Scholar
• Dickerson SS, Stone VI, Panchura C, et al. The meaning of communication: experiences with augmentative communication devices. Rehabil Nurs. 2002;27(6):215–220.
   PubMedGoogle Scholar
• Williams MB, Krezman C, McNaughton D. “Reach for the stars”: five principles for the next 25 years of AAC. Augment Altern Commun. 2008;24(3):194–206.
   Web of Science ®Google Scholar
• Grübler G, Al-Khodairy A, Leeb R, et al. Psychosocial and ethical aspects in non-invasive EEG-based BCI research—a survey among BCI users and BCI professionals. Neuroethics. 2014;7(1):29–41.
   Web of Science ®Google Scholar
• Klein E, Rubel A. Privacy and ethics in brain–computer interface research. Brain–computer interfaces handbook. London, UK: CRC Press; 2018.
   Google Scholar
• MacDuffie KE, Ransom S, Klein E. Neuroethics inside and out: a comparative survey of neural device industry representatives and the general public on ethical issues and principles in neurotechnology. AJOB Neurosci. 2021;13(1):1–11.
   Google Scholar
• Naufel S, Klein E. Brain–computer interface (BCI) researcher perspectives on neural data ownership and privacy. J Neural Eng. 2020;17(1):016039.
   PubMed Web of Science ®Google Scholar
• Pham M, Goering S, Sample M, et al. Asilomar survey: researcher perspectives on ethical principles and guidelines for BCI research. Brain-Comput Interfaces. 2018;5(4):97–111.
   Web of Science ®Google Scholar
• Schönau A, Dasgupta I, Brown T, et al. Mapping the dimensions of agency. AJOB Neurosci. 2021;12(2–3):172–186.
   PubMedGoogle Scholar
• DeCew J. Privacy. In: Zalta EN, editor. Stanford encyclopedia of philosophy. Palo Alto, CA: Metaphysics Research Lab, Stanford University; 2018.
   Google Scholar
• Kraat AW. Communication interaction between aided and natural speakers: a state of the art report. Second edition. Madison, Wisconsin: Trace Center, University of Wisconsin-Madison, Waisman Center, 1500 Highland Ave; 1987.
   Google Scholar
• Fried-Oken M, Fox L, Rau MT, et al. Purposes of AAC device use for persons with ALS as reported by caregivers. Augment Altern Commun. 2006;22(3):209–221.
   PubMed Web of Science ®Google Scholar
• Light J, McNaughton D. Communicative competence for individuals who require augmentative and alternative communication: a new definition for a new era of communication? Augment Altern Commun. 2014;30(1):1–18.
   PubMed Web of Science ®Google Scholar
• Caron J, Light J. “My world has expanded even though I’m stuck at home”: experiences of individuals with amyotrophic lateral sclerosis who use augmentative and alternative communication and social media. Am J Speech Lang Pathol. 2015;24(4):680–695.
   PubMed Web of Science ®Google Scholar
• Blackstone S, Hunt Berg M. Social networks: a communication inventory for individuals with complex communication needs and their communication partners. Monterey, CA: augmentative Communication. Inc.; 2003.
   Google Scholar
• Baylis F. “I am who I am”: on the perceived threats to personal identity from deep brain stimulation. Neuroethics. 2013;6:513–526.
   PubMed Web of Science ®Google Scholar
• Sample M, Aunos M, Blain-Moraes S, et al. Brain–computer interfaces and personhood: interdisciplinary deliberations on neural technology. J Neural Eng. 2019;16(6):063001.
   PubMed Web of Science ®Google Scholar
• Lane JP, Flagg JL. Translating three states of knowledge–discovery, invention, and innovation. Implement Sci. 2010;5:1–14.
   PubMed Web of Science ®Google Scholar
• Flagg JL, Lane JP, Lockett MM. Need to knowledge (NtK) model: an evidence-based framework for generating technological innovations with socio-economic impacts. Implement Sci. 2013;8:1–10.
   PubMed Web of Science ®Google Scholar
• Branco MP, Pels EG, Nijboer F, et al. Brain-Computer interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers. Disabil Rehabil Assist Technol. 2021;35(3):1–11.
   PubMed Web of Science ®Google Scholar
• Taylor S, Balandin S. The ethics of inclusion in AAC research of participants with complex communication needs. Scand J Disabil Res. 2020;22(1):108–115.
   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(4):299–309.
   PubMed Web of Science ®Google Scholar