Journal of Neurologic Physical Therapy:
Special Interest Articles
Ethical Issues in Brain–Computer Interface Research, Development, and Dissemination
Vlek, Rutger J. MSc; Steines, David MSc; Szibbo, Dyana MSc; Kübler, Andrea Prof.; Schneider, Mary-Jane PhD; Haselager, Pim PhD; Nijboer, Femke PhD
Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, The Netherlands (R.J.V., P.H.); University of Illinois, Department of Electrical and Computer Engineering, Urbana-Champaign, Illinois (D.St.); NeuroSky Brain-Computer Interface Technologies, San Jose, California (D.Sz.); University of Würzburg, Department of Psychology I, Würzburg, Germany (A.K.); Laboratory of Neural Injury and Repair, Wadsworth Center, New York State Department of Health, Albany, and Department of Health Policy, Management and Behavior, School of Public Health, University at Albany, State University of New York, Albany (M.-J.S.); and Human-Media Interaction Group, University of Twente, Enschede, The Netherlands (F.N.).
Correspondence: F. Nijboer, Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O. Box 217, 7500 AE Enschede, the Netherlands; E-mail: Femke.Nijboer@utwente.nl
This study was supported by the BrainGain Smart Mix Programme of the Netherlands Ministry of Economic Affairs and the Netherlands Ministry of Education, Culture and Science and by the Information and Communication Technologies Coordination and Support Action “FutureBNCI” within the FP7 framework, Project Number 248320.
The authors declare no conflict of interest.
The steadily growing field of brain–computer interfacing (BCI) may develop useful technologies, with a potential impact not only on individuals, but also on society as a whole. At the same time, the development of BCI presents significant ethical and legal challenges. In a workshop during the 4th International BCI meeting (Asilomar, California, 2010), six panel members from various BCI laboratories and companies set out to identify and disentangle ethical issues related to BCI use in four case scenarios, which were inspired by current experiences in BCI laboratories. Results of the discussion are reported in this article, touching on topics such as the representation of persons with communication impairments, dealing with technological complexity and moral responsibility in multidisciplinary teams, and managing expectations, ranging from an individual user to the general public. Furthermore, we illustrate that where treatment and research interests conflict, ethical concerns arise. On the basis of the four case scenarios, we discuss salient, practical ethical issues that may confront any member of a typical multidisciplinary BCI team. We encourage the BCI and rehabilitation communities to engage in a dialogue, and to further identify and address pressing ethical issues as they occur in the practice of BCI research and its commercial applications.
A brain–computer interface (BCI) is a system that allows its user to control a machine (e.g., a computer, an automated wheelchair, or an artificial limb) soley with brain activity rather than the peripheral nervous system.1,2 Control with a BCI is initiated when a user performs a specific mental task. A typical BCI combines neurophysiological measurement technology with machine learning software to automatically detect patterns of brain activity that relate to this specific mental task. In most BCIs, the user is provided with a small selection of mental tasks (e.g., imagined movement of left hand, right hand, or foot) that the system is trained to detect. Once the system detects that the user has been performing one of the mental tasks, the corresponding actions are automatically triggered (e.g., move cursor left, move cursor right, or click cursor). The following subsections will provide a short overview of relevant aspects of the technology and current state of the art, leading up to the discussion of ethical aspects.
Implementation of a BCI requires brain activity to be measured. Technology to do so can be categorized as either invasive, such as subdural or epidural electrocorticography or implantation of a multielectrode array, or noninvasive, such as electroencephalography (EEG), magnetoencephalography, functional magnetic resonance imaging, or near-infrared spectroscopy. All of these technologies provide some representation of the brain's activity. The choice of a specific method is often determined by a balance between factors such as health risks, user comfort, signal quality, portability, and cost. “Wet” EEG, which involves the use of conductive gel to improve the electrode's connection to the surface of the scalp, is still the most popular noninvasive method for clinical BCI applications. However, low-cost wireless and “dry” alternatives are emerging rapidly, often aimed at less-critical consumer applications, such as BCI games and entertainment for healthy users. BCI technology brings many promising applications in a variety of clinical and nonclinical areas. However, at present, clinical success with BCI is predominantly achieved with prototypes in research laboratories.
Clinical applications of BCI can be roughly divided into two categories. The first category aims at providing a technological alternative for a user's lost function and can be considered a form of assistive technology. The second category aims at rehabilitation of the user's own neural pathways to restore the lost function (see also Daly and Wolpaw3).
In the category of assistive technology, invasive BCI systems have been reported as specialized at real-time decoding of motor functions in nonhuman primates, sometimes even reproducing the decoded motor activity with a cursor or robotic arm.4–6 Eventually, such technology may develop into neuromotor prostheses for humans with motor disabilities. One successful case has been reported in which neural cursor control was achieved by a person with tetraplegia, who had a multielectrode array implanted in the motor cortex.7
Noninvasive BCI systems, mostly EEG-based, have also been investigated for various assistive applications, of which one of the most popular is communication. Spelling applications, for instance, relying on the so-called P300 response, enable a user to “mentally type” symbols on a screen by focusing on specific symbols in a matrix of randomly flashing symbols (usually letters, punctuation marks, and numbers).8,9 Variations of this application have also been presented for use in the auditory domain,10 which may be relevant to users with a progressive neurodegenerative disease affecting vision in its later stages (e.g., amyotrophic lateral sclerosis [ALS]). One of the key problems in these types of communication applications is speed. To achieve sufficient reliability of the system, repeated measurement of the P300 response is necessary, resulting in reduced speed of the application.11 Attempts have been made to overcome this lack of speed by adding artificial intelligence to the application in the form of automatic word completion or error correction from a dictionary (see, for instance, Ahi et al.12), or even entire graph-based sentence databases (such as described in Geuze et al.13). This increases throughput of language at the cost of verbal freedom. In addition to communication applications, prototypes for wheelchair control14 or three-dimensional cursor control15 have also been reported.
In the category of applications for rehabilitation, various results have been reported with the use of an invasive or noninvasive BCI for rehabilitation of gait after stroke.16–18 In these applications, attempted or imagined movement is detected by a BCI and used to artificially move the user's limb with a robotic aid or by functional electrical stimulation of the muscles. This movement provides the user with proprioceptive and visual feedback of the limb following his or her intention, which, in turn, stimulates neural plasticity and then allows the user's own neural pathways to regain control of the limb. Furthermore, BCI is being investigated as a therapy for various cognitive disorders. This is typically done by feeding back functional magnetic resonance imaging measurements to subjects in real time, allowing them to self-regulate a specified area of the brain. Initial studies have illustrated a wide variety of potential applications in treatment of pain, depression, schizophrenia, tinnitus, emotional disorders, and memory.19–21
Besides current and potential clinical applications of BCI, applications are also being developed for game and entertainment purposes.22 Because of the large number of potential users of such applications, this likely will increase research and development resources, from which clinical applications may benefit as a side effect. Moreover, recent work by Münssinger et al.23 on a BCI painting application illustrated that even entertainment BCIs may have a certain degree of clinical relevance by improving the user's social and expressive potential.
Present Research and Development Issues
The fact that BCI technology for clinical use, although promising, still resides predominantly in research laboratories can be explained by several factors. A key factor is the so-called signal-to-noise ratio in a given measurement modality, where the definition of noise includes any signal (including brain signal) that is not of interest to the discrimination of responses to the specific mental tasks used to control the BCI. Both speed and reliability of a BCI system are dependent on this signal-to-noise ratio. In addition, the signal of interest typically varies greatly among and within subjects over time. This makes the quality of a BCI for a specific individual at a specific time very difficult to predict and may lead to frustration for the user when the BCI suddenly stops working. It also means that most BCI systems require training or adjustment to each subject individually. Finally, artifacts may be contribute to initial success with a prototype but to a difficult transition into clinical applications. Especially with EEG-based BCIs, signals originating from sources other than the brain, such as eye movement and muscle activity, may (sometimes without being noticed) boost BCI performance. When the transition is made from healthy users (which is typically the first group to try a prototype) to persons with disability, some of the performance previously achieved may be lost as a consequence of physical conditions. Much of the ongoing BCI research is focused at finding solutions for the issues described previously.
In the future, BCI has the potential to impact not only individual users but also society as a whole. The research and development of future BCI applications such as BCI computer games, neuroprostheses, online cognitive research, neuromarketing, or cognitive enhancement2 inevitably raise ethical and societal challenges, and a public debate on rights and restrictions is to be expected. Apart from being important in their own right, these ethical debates may substantially influence public acceptance of BCIs and related neurotechnologies.
Nascent neuroethical debates have identified several topics of importance to BCI research, development, and dissemination (see Clausen,24 Haselager et al.,25 Tamburrini,26 Tamburrini and Mattia,27 Fenton and Alpert,28 and Walter29). Some of the ethical issues are well known in medical research and the medical device industry. However, there is also a category of issues that are relatively unique to BCI research. Some issues such as the complicated process of obtaining informed consent from persons with locked-in syndrome (LIS) can readily be identified,24 whereas other issues may be less obvious or concrete (e.g., privacy and mind-reading). One basic distinction that permeates ethical debates on BCI concerns the difference between research and treatment. Ethical issues depend, at least in part, on whether the aim is to apply approved technologies for treatment or to develop technologies. Especially in cases where treatment and research interests conflict, important ethical concerns can arise (see the “Jane” case scenario below).
In an effort to triage issues according to technological imminence and ethical novelty,30 we set out to identify and disentangle ethical issues related to BCI use in four case scenarios that were inspired by current experiences in BCI laboratories. Six panel members from various BCI laboratories and companies discussed each case scenario in the workshop on “Ethical Issues in BCI Research, Development, and Dissemination,” which took place at the 4th International BCI Meeting (Asilomar, California, 2010). Results of the discussion are reported in this article.
We have chosen to discuss ethical issues, driven by the case scenarios, in an attempt to illustrate how issues relating to moral responsibility can, in different ways, confront researchers, clinicians, and developers with backgrounds in any of the various disciplines typically involved in BCI research or application. Because the case scenarios are related to issues confronting BCI teams now (cases 1–3) and potentially in the near future (case 4), they may also provide challenging material for analysis by those interested in the more theoretical ethical debate on neurotechnologies (such as BCI and deep brain stimulation).
Jane is a 46-year-old housewife who has had the neurodegenerative disease ALS for 10 years. She lives at home and has a full-time staff of caregivers. Since the onset of locked-in syndrome 1 year ago, she has not been able to communicate in any way. She has a legal representative who enrolled her in tests with noninvasive brain-computer interfaces. The researchers using noninvasive BCI say they can see that Jane is making an effort but that they are unable to reliably decode Jane's brain activity. Jane's husband, who is eager to communicate with his wife again, has read about invasive BCIs in the media and would like to try this method. He asks the BCI team whether his wife could be considered for brain surgery.
Jane's case illustrates BCI research with the most vulnerable participants, namely those who cannot communicate. In such situations, a surrogate decision maker or legal representative is needed to represent the participant. Ethics in this regard are partially codified by law. There are different international regulations specifying when a person is considered to be legally incompetent and who should then be the legal representative. In the United States and the Netherlands, a close relative is a preferred representative, whereas in Germany the preferred representative is a neutral person. Such differences between countries prevent a straightforward global discussion of the ethical aspects of BCI. Other legal issues include the liability of research teams. Currently, initiatives are under way to systematically gather information about international legal practices in relation to neuroscience and neurotechnologies (see, for example, Nadelhoffer31). Furthermore, depending on national legislation, a conflict of interest may occur when close relatives legally represent a person who is unable to communicate. Caregivers and family members tend to underestimate the quality of life of persons with disability32 and may choose not to let their loved one “endure” additional BCI training. Alternatively, caregivers and family members may be so desperate to communicate with their loved one that they would accept just about any intervention offered to them.
The second topic raised in Jane's case is how a BCI team should reply to the request from the husband of a person with complete locked-in syndrome (CLIS) to try a BCI intervention, assuming that the legal representative (if this is not the husband himself) also shows an interest in the BCI intervention. It is common for BCI research laboratories to receive such requests for intervention from people (when not yet CLIS) or from their legal representatives. Researchers may also have an interest in working with persons with CLIS, for example, for understanding commonalities and differences between healthy user groups and user groups with disabilities, thus creating a situation of mutual interest between potential user or representative and researcher. Despite their mutual interest, both parties often do not share the same goal. Motivation from a research perspective can conflict with a therapeutic interest, sometimes leading to a “therapeutic misconception” of the subjects participating in the research. Subjects then “fail to distinguish between clinical care and research and to understand the purpose and aim of research, thereby misconceiving their participation as therapeutic in nature.”33 When research is confused with therapy, the participants or their legal representatives might fail to make decisions that are in the best interests of the participants; in addition, researchers might not adequately understand the motives of their participants.
From a broader perspective, there is also another ethical aspect to BCI treatment and research. At present, any BCI intervention would draw from scientific resources, for which responsibility lies with the researcher or the research group. A moral aspect of this responsibility yields a balancing act between the freedom and well-being of the individual versus that of the public. Because of typical interindividual differences between BCI users, resources spent on improving a system for a single user do not necessarily improve BCI for other users, whether these are individuals in the same disability group or in any of the other potential BCI target groups. Moreover, the balance between individual and public welfare may be skewed if pressure is put on access to experimental assistive technology by users who face the prospect of communication impairments or physical disabilities. A similar pressure was observed regarding experimental medication for acquired immunodeficiency syndrome, which led to public debate and lawsuits concerning the public right to experimental treatment (see, for example, Richman34 and Leonard35).
Nigel is a 51-year-old research scientist who has had ALS for 11 years. He has used a P300 BCI home system for 4 years to communicate with his family members and for professional purposes with his laboratory colleagues. In recent months, he has dramatically reduced his use of the system and appears to be losing the capability to control it. The BCI team has noticed the decline and is trying hard to determine whether the algorithms need to be adapted.
Unlike the first case scenario, Nigel is able to communicate. Persons with LIS like Nigel may also have a legal representative who must give the legally necessary informed consent. In addition, a person with LIS may still be able to give assent. Where this is possible, even by only signaling “yes” via eye blinks or other muscle twitches, researchers must seek that assent in addition to the consent of the legal representative, and dissent should be respected.25,36
Second, the information regarding what to expect from a BCI system is crucial for giving informed consent. In this case scenario, the subject should have been informed about a possible decline in BCI performance, but managing expectations of a BCI proves to be difficult. Although a BCI system, in principle, yields numerous useful applications for a user, factors such as intersubject differences and the brain's plasticity make it hard to predict BCI success for a specific user. The multidisciplinary nature and typical size of a BCI research team contributes to this problem. Various members of a team may assess expectations of a system differently. Haselager et al25 point out the similarity with other interdisciplinary teams working in similarly demanding situations, such as intensive care or mental health care. Furthermore, BCI performance may be related to physical decline.37 If this is the case, informing the user with disability about BCI expectations implies that information is also provided regarding progress of the disease, which the person may not (yet) want to know about.38 Finally, overly enthusiastic media coverage about BCIs could heighten an individual's expectations of BCI, further undermining informed consent.39
A third issue that arises when visiting persons with severe paralysis or locked-in syndrome at home is that BCI training inevitably interferes with their care and daily life (and the lives of their family and/or caregivers). This is a factor that researchers should include in moral considerations. For example, a person with LIS who has phantom limb pain reduced her morphine intake on days when she was training with a P300 BCI, because she knew that morphine could reduce detection of the P300. Participants may opt to accept discomfort, changes in care schedules, or even pain to be able to work with the BCI. For this reason, the interference of BCI with daily life and care is a potential ethical concern. Information about the extent of interference with daily life is crucial for weighing positive and negative effects in making decisions about BCI intervention. Participants in BCI studies often report feeling positively challenged by training. For example, subject H., who participated over 2 years in three BCI studies, stated that he looked forward to every training session, because he knew he could “work with his head” and that he was mentally as healthy as other people.40 A recent study showed that most participants were highly motivated throughout BCI training and their mood was often good before training.41
Nine months ago Ben had a stroke, resulting in paralysis of his right arm. He has regained some function in his arm after months of extensive motor rehabilitation, and Ben's doctor asked him to enroll in a study that investigates whether BCI neurofeedback could accelerate Ben's recovery. Ben has difficulty understanding what the doctor is asking of him (he has minor cognitive impairment), but he trusts his doctor.
Ben's case refers to more recent BCI studies, which investigated the effects of neurofeedback training on the rehabilitation process after stroke.3,16,42–44 The combination of acutely ill and vulnerable participants demands an especially careful evaluation of risk and benefit, the process of consent, and the permissible treatment of control participants.45 The question could be raised whether Ben, given his cognitive impairment, is really the most suitable subject for the study. In this situation, merely presenting Ben the option of BCI intervention may already carry some coercive weight. The issue of coercion is not unique to BCI and has been empirically addressed in the literature.46 Furthermore, this case emphasizes the need for clear inclusion and exclusion criteria for any study to be performed with participants in possible need of BCI.
A second issue relates to possible side effects of BCI intervention and, more specifically, neurofeedback training. No adverse side effects of BCI intervention and training have been reported, but no systematic research exists on this topic. Some BCI teams exclude study participants with epilepsy, which may be of relevance in studies involving flickering visual stimuli at specific frequencies and high contrasts, such as the stimuli sometimes used to evoke a P300 or SSVEP (steady-state visual evoked potential) response. In a neurofeedback study, Hoedlmoser et al.47 noted an impact of sensorimotor rhythm training on sleep and declarative learning. Their training paradigm is very similar to a paradigm regularly used in the BCI community.48,49 Regarding the use of the brain's plasticity for obtaining or improving BCI control or rehabilitation, one of the panel members remarked that if we can do any good, we can certainly also do harm. The question here is: how different is mental training in BCI from any other forms of training with which we are already very familiar (e.g., driver training or subject preparations for psychological experiments), and are any of these differences cause for ethical concern? We recommend that in future clinical trials all positive and adverse side effects be systematically investigated.
Beyond rehabilitation of gait after stroke, the principle of neurofeedback or self-regulation is being investigated for treatment of various neurological or cognitive disorders, as discussed in the introduction to this report. The increasing number of potential applications in this therapeutical area brings us into a different domain of ethical questions, regarding identity and change of personality and self-perception.26,50 When, for instance, BCI is used for rehabilitation of an emotional disorder, one could wonder how this affects personality, albeit as a side effect or as the main effect. A similar type of neurotechnology that is currently being investigated for a variety of diseases such as psychiatric disorders, Alzheimer's, and Parkinson's disease is deep brain stimulation. The ethical debate surrounding deep brain stimulation technology is addressing similar issues of identity and personality.51,52
Thomas is a 30-year-old air traffic controller who was told by his boss that starting this month he would have to undergo attention training by wearing a new neurotechnological tool that provides him with neurofeedback. Thomas does not know exactly what the device does, but he feels that his attention has somewhat improved since he started training. The explanation that Thomas was given about the new tool was that it somehow reads his brain, so he is sometimes afraid the tool can also read his thoughts. Also, last Monday Thomas got a lecture from his boss, who said he could see that Thomas most likely had been drinking alcohol on Sunday night.
The scenario of Thomas (albeit somewhat futuristic) touches upon more philosophical topics such as extended28 and enacted mind,29 but most importantly it raises concern for mind reading and privacy. As this case scenario illustrates, employers may be able to gain more information than had been agreed upon when they require an employee to use a BCI system. Furthermore, the subject may be completely unaware of the extent of information that is being obtained from his or her brain.
In this case, the employer was able to discern the fact that the subject might have been drinking the night before, but more generally a BCI system may be able to reveal other psychological states, traits, and mental health vulnerabilities.53 It may not be in an individual's best interest to have this personal information available to others, especially an employer, and workplace discrimination could be a concern. It could be seen as a violation of a person's right to privacy. Furthermore, the case scenario raises concerns about social stratification and brain enhancement.54,55 If brain enhancement does become effective and popular, there could be pressure to enhance one's brain to keep up with the competition. Barriers such as cost could prevent some people from accessing this enhancement. This issue, though complicated, is not unique to BCI and is further discussed elsewhere.25,26,30,53
BCI may lead to useful technologies for humankind but at the same time present significant ethical and legal challenges. The challenges are due to several factors: BCI is a rapidly growing research area with potential future applications of great daily significance, for both medical and regular users, that is bound to attract the attention of media and commercial enterprises.
In the four case scenarios, we have identified ethical issues related to the application of BCI for users with disabilities and healthy users. Issues typical to the field of BCI relate to working with sensitive user groups, dealing with technological complexity, and handling multidisciplinary teams. We illustrated that when treatment and research interests conflict, ethical concerns arise. Managing the expectations of this novel technology is important on different levels, ranging from individual users to legal representatives to the public (via the media). We encourage researchers to facilitate ethical and public debate and keep expectations in line with achievements, because the future success of both BCI research and commercial applications will rely on public acceptance of the technology.
Practical recommendations from the panel members consist of creating more awareness of ethical aspects in the BCI field, which involves reaching members of all disciplines in a typical BCI multidisciplinary team. To facilitate discussion and sharing of information on ethics, we recommend the organization of an ethics workshop or discussion group in future field-specific conferences. Moreover, we suggest inviting ethicists and philosophers to participate in this dialogue. We hope that these recommendations, as well as this report, will contribute to the ability to identify and address pressing ethical issues as they appear in the rapidly progressing BCI research and commercialization.
The authors thank Jonathan Wolpaw and Leigh Hochberg.
1. Dornhege G, del R, Millán J, Hinterberger T, McFarland D, Müller KR, eds. Toward Brain-Computer Interfacing. Cambridge, MA: MIT Press; 2007.
2. Van Gerven M, Farquhar J, Schaefer R, et al. The brain-computer interface cycle. J Neural Eng. 2009;6(4):041001.
3. Daly J, Wolpaw J. Brain-computer interfaces in neurological rehabilitation. Lancet Neural. 2008;7:1032–1043.
4. Wessberg J, Stambaugh C, Kralik J, et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature. 2000;408:361–365.
5. Carmena J, Lebedev M, Crist R, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 2003;1(2): 0000042.
6. Serruya M, Hatsopoulos N, Paninski L, Fellows M, Donoghue J. Brain-machine interface: Instant neural control of a movement signal. Nature. 2002;416:141–142.
7. Kim S, Simeral J, Hochberg L, Donoghue J. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J Neural Eng. 2008;5(4):455–476.
8. Sellers E, Donchin E. A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol. 2006;117:538–548.
9. Nijboer F, Sellers E, Mellinger J, et al. A brain-computer interface (BCI) for people with amyotrophic lateral sclerosis (ALS). Clin Neurophysiol. 2008;119(8):1909–1916.
10. Schreuder M, Blankertz B, Tangermann M. A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS One. 2010;5(4):e9813.
11. Zickler C, Riccio A, Leotta F, et al. A brain-computer interface as input channel for a standard assistive technology software. Clin EEG Neurosci. 2011;42(4):222–230.
12. Ahi S, Kambara H, Koike Y. A dictionary-driven p300 speller with a modified interface. IEEE Trans Neural Syst Rehabil Eng. 2011;19(1):6–14.
13. Geuze J, Desain P, Ringelberg J. Re-phrase: chat-by-click: a fundamental new mode of human communication over the Internet. In: CHI ‘08 extended abstracts on Human factors in computing systems. CHI EA ‘08; New York, NY: ACM; 2008:3345–3350.
14. Vanacker G, del R, Millán J, et al. Context-based filtering for assisted brain-actuated wheelchair driving. Comput Intel Neurosci. 2007;25130.
15. McFarland D, Sarnacki W, Wolpaw J. Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng. 2011;7(3):036007.
16. Buch E, Weber C, Cohen L, et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. 2008;39:910–917.
17. Daly J, Cheng R, Rogers J, Litinas K, Hrovat K, Dohring M. Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther. 2009;33(4):203–211.
18. Rodriguez MG, Grosse-Wentrup M, Hill J, Gharabaghi A, Schölkopf B, Peters J. Towards brain-robot interfaces in stroke rehabilitation. In: Proceedings of 12th International Conference on Rehabilitation Robotics. 2011:1–6.
19. DeCharms R, Maeda F, Glover G, et al. Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci U S A.2005;102:18626–18631.
20. Weiskopf N. Real-time fMRI and its application to neurofeedback [published online ahead of print October 14, 2011]. Neuroimage. In press. doi:10.1016/j.neuroimage.2011.10.009.
21. Zotev V, Krueger F, Phillips R, et al. Self-regulation of amygdala activation using real-time fMRI neurofeedback. PLoS One. 2011;6(9):e24522.
22. Nijholt A, Bos DPO, Reuderink B. Turning shortcomings into challenges: brain-computer interfaces for games. Entertain Comput. 2009;1(2):85–94.
23. Münssinger JI, Halder S, Kleih S, et al. Brain painting: evaluation of a new brain-computer interface application with ALS patients and healthy volunteers. Front Neurosci. 2010;4(182).
24. Clausen J. Man, machine and in between. Nature. 2009;457:1080–1081.
25. Haselager P, Vlek R, Hill J, Nijboer F. A note on ethical aspects of BCI. Neural Netw. 2009;22(9):1352–1357.
26. Tamburrini G. Brain to computer communication: ethical perspectives on interaction models. Neuroethics. 2009;2:137–149.
27. Tamburrini G, Mattia D. Disorders of consciousness and communication: ethical motivations and communication-enabling attributes of consciousness. Funct Neurol. 2011;26(1):51–54.
28. Fenton A, Alpert S. Extending our view on using BCIs for locked-in syndrome. Neuroethics. 2008;1(2):119–132.
29. Walter S. Locked-in syndrome, BCI, and confusion about embodied, embedded, extended and enacted cognition. Neuroethics. 2010;3:61–72.
30. Farah M. Emerging ethical issues in neuroscience. Nat Neurosci. 2002;5:1123–1129.
32. Kübler A, Winter S, Ludolph A, Hautzinger M, Birbaumer N. Severity of depressive symptoms and quality of life in patients with amyotrophic lateral sclerosis. Neurorehabil Neural Repair. 2005;19(3):182–193.
33. Beauchamp T, Childress J. Principles of Biomedical Ethics. 6th ed. Oxford New York: University Press; 2008:129.
34. Richman D. Public access to experimental drug therapy: AIDS raises yet another conflict between freedom of the individual and welfare of the individual and public. J Infect Dis. 1989;159:412–415.
35. Leonard E. Right to experimental treatment: FDA new drug approval, constitutional rights, and the public's health. J Law Med Ethics. 2009;37:269–279.
37. Kübler A, Birbaumer N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol. 2008;19(11):2658–2666.
38. Borasio G, Sloan R, Pongratz D. Breaking the news in amyotrophic lateral sclerosis. J Neurol Sci. 1998;160(1):S127-S133.
39. Nijboer F, Clausen J, Allison B, Haselager P. The Asilomar survey: stakeholders' opinions on ethical issues related to brain-computer interfacing [published online ahead of print October 20, 2011]. Neuroethics. DOI: 10.1007/s12152-011-9132-6.
40. Nijboer F, Kleih S, Kübler A. Das technisierte Gehirn; chap. Gehirn-Computer Schnittstellen Fuer Schwerstgelaehmte Menschen - klinische Moeglichkeiten, Technische Grenzen Und Etische Fragen. Paderborn: Mentis Verlag GmbH; 2009:51–64.
41. 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 Neuroprosthet. 2010;4:55.
42. Rozelle G, Budzynski T. Neurotherapy for stroke rehabilitation: a single case study. Biofeedback Self Regul. 1995;20(3):211–228.
43. Pfurtscheller G, Neuper C. Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. Prog Brain Res. 2006;159:433–437.
44. Prasad G, Herman P, Coyle D, McDonough S, Crosbie J. Using a motor imagery based brain-computer interface for post-stroke rehabilitation. In: Proceedings of 4th international IEEE/EMB conference on Neural Engineering. 2009:258–262.
45. Slyter H. Ethical challenges in stroke research. Stroke. 1998;29:1725–1729.
46. Rain S, Williams V, Robbins P, Monahan J, Steadman H, Vesselinov R. Perceived coercion at hospital admission and adherence to mental health treatment after discharge. Psychiatr Serv. 2003;54(1):103–105.
47. Hoedlmoser K, Pecherstorfer T, Gruber G, et al. Instrumental conditioning of human sensorimotor rhythm (12–15 Hz) and its impact on sleep as well as declarative learning. Sleep. 2008;31(10):1401–1408.
48. Nijboer F, Furdea A, Gunst I, et al. An auditory brain-computer interface (BCI). J Neurosci Methods. 2008;167:43–50.
49. McFarland D, Miner L, Vaughan T, Wolpaw J. Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr. 2000;12:177–186.
50. Hildt E. Brain-computer interaction and medical access to the brain: individual, social and ethical implications. Stud Ethics Law Technol. 2010;4(3):1–20. doi:10.2202/1941-6008.1143.
51. Klaming L, Haselager P. Did my brain implant make me do it? Questions raised by DBS regarding psychological continuity, responsibility for action and mental competence. Neuroethics. September 16th, 2010. DOI 10.1007/s12152-010-9093-1.
52. Clausen J. Conceptual and ethical issues with brain-hardware interfaces. Curr Opin Psychiatry. 2011;24(6):495–501.
53. Farah M. Neuroethics: the practical and the philosophical. Trends Cogn Sci. 2005;9(1):34–40.
54. Caplan A, Elliott C. Is it ethical to use enhancement technologies to make us better than well? PLoS Med. 2004;1(3).
55. Forlini C, Racine E. Autonomy and coercion in academic “cognitive enhancement” using methylphenidate: perspectives of key stakeholders. Neuroethics. 2009;2:163–177.
brain–computer interface; ethics; locked-in syndrome; neuroethics
© 2012 Neurology Section, APTA
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