SPECIAL INTEREST ARTICLES Ethical Issues in Brain–Computer Interface Research, Development, and Dissemination Rutger J. Vlek, MSc, David Steines, MSc, Dyana Szibbo, MSc, Andrea K¨ ubler, Prof., Mary-Jane Schneider, PhD, Pim Haselager, PhD, and Femke Nijboer, PhD 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 de- velopment of BCI presents significant ethical and legal challenges. In a workshop during the 4th International BCI meeting (Asilomar, Cal- ifornia, 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 experi- ences 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 scenar- ios, 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. Key Words: brain–computer interface, ethics, locked-in syndrome, neuroethics (JNPT 2012;36: 94–99) Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, The Netherlands (R.J.V., P.H.); University of Illinois, De- partment of Electrical and Computer Engineering, Urbana-Champaign, Illinois (D.St.); NeuroSky Brain-Computer Interface Technologies, San Jose, California (D.Sz.); University of W¨ urzburg, Department of Psy- chology I, W¨ urzburg, 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.). 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. 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 Copyright C  2012 Neurology Section, APTA. ISSN: 1557-0576/12/3602-0094 DOI: 10.1097/NPT.0b013e31825064cc INTRODUCTION Definition A brain–computer interface (BCI) is a system that al- lows its user to control a machine (e.g., a computer, an auto- mated 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 tech- nology 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. Measurement Techniques 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 spec- troscopy. All of these technologies provide some representa- tion 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 appli- cations. However, low-cost wireless and “dry” alternatives are emerging rapidly, often aimed at less-critical consumer ap- plications, 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 pro- totypes in research laboratories. Applications Clinical applications of BCI can be roughly divided into two categories. The first category aims at providing a Copyright © 2012 Neurology Section, APTA. Unauthorized reproduction of this article is prohibited. 94 JNPT  Volume 36, June 2012