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The Risks Associated with the Use of Brain-Computer Interfaces: A Systematic Review
International Journal of Human-Computer Interaction
Abstract
Brain-computer interfaces (BCI) are an emerging technology that can read the brain signals of users, derive behavioral intentions, and manifest them in the control of electronic technologies. While there are potentially great benefits of this technology, there may also be risks associated with their use. However, it is not clear if these risks are being considered in the early BCI literature. This systematic review aimed to identify the scope and types of BCI risks discussed in the literature and the methods used to identify these risks. Following the PRISMA protocol, 1184 articles were initially identified with the final selection of 58 published articles following systematic exclusion. Analysis of the included articles derived 20 different risks, which were categorized into seven risk themes spanning physical health risks through to legal and societal concerns. Only one study in the review used a method of risk assessment, with most articles identifying risks through discussion and opinion pieces. The findings highlight a lack of an empirical and comprehensive understanding of the risks that BCI technology could pose. It is concluded that further work is necessary to proactively identify BCI risks using formal risk assessment methods to inform early users and to direct risk control measures for BCI developers and regulators.
... Historically, ethics were not a focus in reviews, although the procedures are more invasive than modern to BCI systems [32,46]. Te most discussed ethical issues that surround BCI are user safety and risk benefts associated with their use, particularly those surrounding the use of invasive and semi-invasive technologies [22,73,94]. However, there are three areas in need of ethical consideration when dealing with BCI applications and their users: (1) Te ability of an individual that utilizes a BCI system to provide informed consent and their loss of personhood. ...
... Similarly, words associated with the level of control an individual has in relation to the synchronous systems were not themes identifed in the word frequency analysis. Much of the ethical debate on BCI research revolves around informed consent, privacy, risk, and security of the individual; again, these were lacking in any form in both the word cloud and its associated word cluster analysis [73,90,94]. It can be argued that ethical issues are not seen as structurally important, rather based on the lack of thematic detection, may be said to only extend to the adherence to the conditions surrounding institutional approvals that pertain to specifc projects. ...
This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of “normal” individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.
... In patients with locked-in syndrome, the morality of the use of BCIs requires more analysis and collaboration between the parties involved [38]. In the context of informed consent, it is important that all potential risks are analyzed in a way that is understandable to the user [39]. Although the responsibility for informed consent lies with the researcher, the responsibility for identifying and investigating risks belongs to all participants in the BCIs field [9]. ...
... At the same time, the pursuit of lighter and more comfortable devices limits computing power and battery life. Although BCI can enhance the ease of interaction effectively, it faces a high risk of surgery and human tissue rejection [65]. Non-invasive BCI can partially mitigate the safety risks associated with complex surgery, but it faces challenges due to weak signal collection and limited dissemination. ...
Human-computer interaction (HCI) emerged with the birth of the computer and has been upgraded through decades of development. Metaverse has attracted a lot of interest with its immersive experience, and HCI is the entrance to the Metaverse for people. It is predictable that HCI will determine the immersion of the Metaverse. However, the technologies of HCI in Metaverse are not mature enough. There are many issues that we should address for HCI in the Metaverse. To this end, the purpose of this paper is to provide a systematic literature review on the key technologies and applications of HCI in the Metaverse. This paper is a comprehensive survey of HCI for the Metaverse, focusing on current technology, future directions, and challenges. First, we provide a brief overview of HCI in the Metaverse and their mutually exclusive relationships. Then, we summarize the evolution of HCI and its future characteristics in the Metaverse. Next, we envision and present the key technologies involved in HCI in the Metaverse. We also review recent case studies of HCI in the Metaverse. Finally, we highlight several challenges and future issues in this promising area.
... For example, ethical and legal issues have been identified in respect to autonomous systems (Burton et al., 2020) and artificial general intelligence (AGI) systems (McLean et al., 2021). Investigations in other types of emerging technologies, such as brain-computer interfaces, have been suggested to pose risks associated with personal autonomy, legal considerations, privacy and security, psychological impacts, physical health, societal impacts, and malicious applications (King, Read & Salmon, 2022). Given the potential application of these technologies to transport systems of the future, either for driver interfaces or traffic management, it is germane to consider potential risks that these technologies might pose. ...
Road transport is experiencing disruptive change from new first-of-a-kind technologies. While such technologies offer safety and operational benefits, they also pose new risks. It is critical to proactively identify risks during the design, development and testing of new technologies. The Systems Theoretical Accident Model and Processes (STAMP) method analyses the dynamic structure in place to manage safety risks. This study applied STAMP to develop a control structure model for emerging technologies in the Australian road transport system and identified control gaps. The control structure shows the actors responsible for managing risks associated with first-of-a-kind technologies and the existing control and feedback mechanisms. Gaps identified related to controls (e.g., legislation) and feedback mechanisms (e.g., monitoring for behavioural adaptation). The study provides an example of how STAMP can be used to identify control structure gaps requiring attention to support the safe introduction of new technologies.
In recent years, the rapid proliferation of Brain-Computer Interface (BCI) applications has made the issue of security increasingly important. User authentication serves as the cornerstone of any secure BCI systems, and among various methods, EEG-based authentication is particularly well-suited for BCIs. However, existing paradigms, such as visual evoked potentials and motor imagery, demand significant user efforts during both enrollment and authentication phases. To address these challenges, we introduce a novel paradigm–Keystroke Evoked Potentials (KEP) for EEG-based authentication, which is secure, user-friendly, and lightweight. Then, we design an authentication system based on our proposed KEP. The core concept involves generating a shared cryptographic session key derived from EEG data and keystroke dynamics captured during random button-pressing activities. This shared key is subsequently employed in a Diffie-Hellman Encrypted Key Exchange (DH-EKE) to facilitate device pairing and establish a secure communication channel. Based on a collected dataset, the results demonstrate that our system is secure against various attacks (e.g., mimicry attack, replay attack) and efficient in practice (e.g., taking only 0.07 s to generate 1 bit).
One of the most crucial use of hands in daily life is grasping. Sometimes people with neuromuscular disorders become incapable of moving their hands. This paper proposes a grasp motor imagery identification approach based on multivariate fast iterative filtering (MFIF). The proposed methodology involves the selection of relevant electroencephalogram (EEG) channels based on the neurophysiology of the brain. The selected EEG channels have been decomposed into five components using MFIF. Information potential based features are extracted from the decomposed EEG components. The extracted features are smoothed using a moving average filter. The smoothed features are classified using the k-nearest neighbors classifier. The cross-subject classification accuracy, precision, and F1-score of 98.25%, 98.31%, and 98.24% respectively is obtained. While the average classification accuracy, precision and F1-score for multiple subjects is 98.43%, 98.62% and 98.41% respectively. The proposed methodology can be used for the development of a low cost EEG based grasp identification system.
Brain-computer interfaces (BCIs) are a rapidly developing technology with the potential for a wide range of applications, yet the technology and its possible risks are poorly understood. The aim of this study was to develop and analyse an abstraction hierarchy model of a hypothetical, advanced invasive BCI system lifecycle to identify potential design lifecycle risks. Ten subject matter experts participated in a workshop to validate a BCI lifecycle abstraction hierarchy, which was subsequently analysed using the Sociotechnical Systems-Design Toolkit (STS-DT) prompts. The analysis identified the system purpose of generating wealth for companies and shareholders conflicted with other system purposes relating to health and wellbeing. Findings also identified poorly supported system functions, such as the regulation of BCI technologies, and potentially unreliable system objects. In conclusion, it is recommended that the identified issues be addressed through a sociotechnical systems approach focusing on joint optimisation across the system.
Brain-computer interface (BCI) technologies are progressing rapidly and may eventually be implemented widely within society, yet their risks have arguably not yet been comprehensively identified, nor understood. This study analysed an anticipated invasive BCI system lifecycle to identify the individual, organisational, and societal risks associated with BCIs, and controls that could be used to mitigate or eliminate these risks. A BCI system lifecycle work domain analysis model was developed and validated with 10 subject matter experts. The model was subsequently used to undertake a systems thinking-based risk assessment approach to identify risks that could emerge when functions are either undertaken sub-optimally or not undertaken at all. Eighteen broad risk themes were identified that could negatively impact the BCI system lifecycle in a variety of unique ways, while a larger number of controls for these risks were also identified. The most concerning risks included inadequate regulation of BCI technologies and inadequate training of BCI stakeholders, such as users and clinicians. In addition to specifying a practical set of risk controls to inform BCI device design, manufacture, adoption, and utilisation, the results demonstrate the complexity involved in managing BCI risks and suggests that a system-wide coordinated response is required. Future research is required to evaluate the comprehensiveness of the identified risks and the practicality of implementing the risk controls.
Artificial General intelligence (AGI) offers enormous benefits for humanity, yet it also poses great risk. The aim of this systematic review was to summarise the peer reviewed literature on the risks associated with AGI. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen articles were deemed eligible for inclusion. Article types included in the review were classified as philosophical discussions, applications of modelling techniques, and assessment of current frameworks and processes in relation to AGI. The review identified a range of risks associated with AGI, including AGI removing itself from the control of human owners/managers, being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values; inadequate management of AGI, and existential risks. Several limitations of the AGI literature base were also identified, including a limited number of peer reviewed articles and modelling techniques focused on AGI risk, a lack of specific risk research in which domains that AGI may be implemented, a lack of specific definitions of the AGI functionality, and a lack of standardised AGI terminology. Recommendations to address the identified issues with AGI risk research are required to guide AGI design, implementation, and management.
Objective:
Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI.
Methods:
Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control.
Results:
Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home.
Significance:
Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.
Stimulant drugs, transcranial magnetic stimulation, brain-computer interfaces, and even genetic modifications are all discussed as forms of potential cognitive enhancement. Cognitive enhancement can be conceived as a benefit-seeking strategy used by healthy individuals to enhance cognitive abilities such as learning, memory, attention, or vigilance. This phenomenon is hotly debated in the public, professional, and scientific literature. Many of the statements favoring cognitive enhancement (e.g., related to greater productivity and autonomy) or opposing it (e.g., related to health-risks and social expectations) rely on claims about human welfare and human flourishing. But with real-world evidence from the social and psychological sciences often missing to support (or invalidate) these claims, the debate about cognitive enhancement is stalled. In this paper, we describe a set of crucial debated questions about psychological and social aspects of cognitive enhancement (e.g., intrinsic motivation, well-being) and explain why they are of fundamental importance to address in the cognitive enhancement debate and in future research. We propose studies targeting social and psychological outcomes associated with cognitive enhancers (e.g., stigmatization, burnout, mental well-being, work motivation). We also voice a call for scientific evidence, inclusive of but not limited to biological health outcomes, to thoroughly assess the impact of enhancement. This evidence is needed to engage in empirically informed policymaking, as well as to promote the mental and physical health of users and non-users of enhancement.
The next generation of artificial intelligence, known as artificial general intelligence (AGI) could either revolutionize or destroy humanity. As the discipline which focuses on enhancing human health and wellbeing, human factors and ergonomics (HFE) has a crucial role to play in the conception, design, and operation of AGI systems. Despite this, there has been little examination as to how HFE can influence and direct this evolution. This study uses a hypothetical AGI system, Tegmark's “Prometheus,” to frame the role of HFE in managing the risks associated with AGI. Fifteen categories of HFE method are identified and their potential role in AGI system design is considered. The findings suggest that all categories of HFE method can contribute to AGI system design; however, areas where certain methods require extension are identified. It is concluded that HFE can and should contribute to AGI system design and immediate effort is required to facilitate this goal. In closing, we explicate some of the work required to embed HFE in wider multi‐disciplinary efforts aiming to create safe and efficient AGI systems.
The brain-computer interface (BCI) system is doing wonders for people suffering from restricted physical abilities due to accidents or diseases. BCI requires recording of electroencephalogram (EEG) from the subject in order to control electrical devices. In the wireless BCI system, there is always a possibility of EEG signal tampering/attacking during transmission, which may result in malfunction of the BCI system. This work introduces a security block in the BCI system that can ensure that the recorded EEG signals are intact at the receiving end of wireless BCI system. The security block identifies any tampering as well as authenticates the EEG signals at receiving end. Any change in EEG signal can result into classification error; because of that prediction error expansion based reversible watermarking approach has been used. The proposed scheme works efficiently and performance of BCI system remains unaltered after inclusion of security block.
Deep brain stimulation (DBS) is a neurosurgical procedure that allows targeted circuit-based neuromodulation. DBS is a standard of care in Parkinson disease, essential tremor and dystonia, and is also under active investigation for other conditions linked to pathological circuitry, including major depressive disorder and Alzheimer disease. Modern DBS systems, borrowed from the cardiac field, consist of an intracranial electrode, an extension wire and a pulse generator, and have evolved slowly over the past two decades. Advances in engineering and imaging along with an improved understanding of brain disorders are poised to reshape how DBS is viewed and delivered to patients. Breakthroughs in electrode and battery designs, stimulation paradigms, closed-loop and on-demand stimulation, and sensing technologies are expected to enhance the efficacy and tolerability of DBS. In this Review, we provide a comprehensive overview of the technical development of DBS, from its origins to its future. Understanding the evolution of DBS technology helps put the currently available systems in perspective and allows us to predict the next major technological advances and hurdles in the field.
The nuclear facilities are operated to give safety the utmost priority and all possible scenarios that may lead to hazardous states must be evaluated. To date, the probabilistic safety assessment has been used as one of the standard tools for the safety evaluation; however, concerns have been raised about its capability to treat the complex interaction between human operators, digital systems, and diverse plant processes. This paper proposes an operational vulnerability identification procedure based on STAMP/STPA (System Theoretic Accident Model and Process/Systems-Theoretic Process Analysis) which allows us to derive unsafe control action (UCA) leading to the unwanted consequence of a system, such as a spurious reactor trip. The effectiveness of the proposed procedure is demonstrated with the case study of a cold neutron source system installed in High-Flux Advanced Neutron Application Reactor (HANARO). In result, 127 UCAs were derived for 51 control actions regarding spurious trip scenario. The UCAs were reviewed by the HANARO operators and found new scenarios that requires further investigation for reducing the possibility of a spurious trip. The proposed procedure is expected to provide a holistic viewpoint for operational vulnerability identification and further used to suggest recommendations for the safety enhancement of nuclear facilities.
Ethical analyses of biomedical human enhancement often consider the issue of authenticity-to what degree can the accomplishments of those utilizing biomedical enhancements (including cognitive or athletic ones) be considered authentic or worthy of praise? As research into Brain-Computer Interface (BCI) technology progresses, it may soon be feasible to create a BCI device that enhances or augments natural human intelligence through some invasive or noninva-sive biomedical means. In this article we will (1) review currently existing BCI technologies and to what extent these can be said to enhance or augment the capabilities of the respective users, (2) describe one hypothetical type of BCI device that could augment or enhance a specific aspect of human knowledge-namely, mathematical ability, and (3) relate these concepts to the active externalism view of the extended mind as es-poused by Clark and Chalmers in order to (4) argue that knowledge of mathematics derived from the usage of a BCI and the application thereof constitutes authentic knowledge and achievement.
Brain-Computer Interfaces (BCI) arose as systems that merge computing systems with the human brain to facilitate recording, stimulation, and inhibition of neural activity. Over the years, the development of BCI technologies has shifted towards miniaturization of devices that can be seamlessly embedded into the brain and can target single neuron or small population sensing and control. We present a motivating example highlighting vulnerabilities of two promising micron-scale BCI technologies, demonstrating the lack of security and privacy principles in existing solutions. This situation opens the door to a novel family of cyberattacks, called neuronal cyberattacks, affecting neuronal signaling. This paper defines the first two neural cyberattacks, Neuronal Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the natural activity of neurons. This work implements these attacks in a neuronal simulator to determine their impact over the spontaneous neuronal behavior, defining three metrics: number of spikes, percentage of shifts, and dispersion of spikes. Several experiments demonstrate that both cyberattacks produce a reduction of spikes compared to spontaneous behavior, generating a rise in temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact than FLO in the metrics focused on the number of spikes and dispersion, where FLO is slightly more damaging, considering the percentage of shifts. Nevertheless, the intrinsic behavior of each attack generates a differentiation on how they alter neuronal signaling. FLO is adequate to generate an immediate impact on the neuronal activity, whereas SCA presents higher effectiveness for damages to the neural signaling in the long-term.
Brain–machine interfaces (BMIs), which enable a two-way flow of signals, information, and directions between human neurons and computerized machines, offer spectacular opportunities for therapeutic and consumer applications, but they also present unique dangers to the safety, privacy, psychological health, and spiritual well-being of their users. The sale of these devices as commodities for profit exacerbates such issues and may subject the user to an unequal exchange with corporations. Catholic healthcare professionals and bioethicists should be especially concerned about the implications for the essential dignity of the persons using the new BMIs.
Summary
The commercial sale of brain-machine interfaces (BMIs) generates and exacerbates problems for end-users' safety, psychological health, and spiritual well-being.
Devices that record from and stimulate the brain are currently available for consumer use. The increasing sophistication and resolution of these devices provide consumers with the opportunity to engage in do-it-yourself brain research and contribute to neuroscience knowledge. The rise of do-it-yourself (DIY) neuroscience may provide an enriched fund of neural data for researchers, but also raises difficult questions about data quality, standards, and the boundaries of scientific practice. We administered an online survey to brain–computer interface (BCI) researchers to gather their perspectives on DIY brain research. While BCI researcher concerns about data quality and reproducibility were high, the possibility of expert validation of data generated by citizen neuroscientists mitigated concerns. We discuss survey results in the context of an established ethical framework for citizen science, and describe the potential of constructive collaboration between citizens and researchers to both increase data collection and advance understanding of how the brain operates outside the confines of the lab.
Brain–computer interfaces (BCIs) provide the measurement of the activities of central nervous systems, and they convert the activities into artificial outputs. Currently, one of the most interesting researches in BCIs is to develop methods for decoding people’s intent from the neural signals. Electroencephalogram (EEG) is one of the ideal solution that provides a portable recording system of neural signals. One of the challenges is to decode people’s intended movement based on EEG. Machine learning is a powerful tools and has been used in BCIs. Due to the large-scale computations, the model training process for machine learning is not always efficient. In order to address such challenges of electroencephalogram modeling in BCIs, we present an efficient machine learning algorithm based on normal equation. First, we propose a systolic matrix multiplication of two matrices. Second, we propose a systolic matrix inversion for large matrix. Third, we propose a systolic matrix-vector multiplication. In addition, the efficiency of the machine learning algorithm is analyzed and its applications of electroencephalogram modeling in BCIs are discussed.
There is evidence to suggest that some patients who undergo Deep Brain Stimulation can experience changes to dispositional, emotional and behavioural states that play a central role in conceptions of personality, identity, autonomy, authenticity, agency and/or self (PIAAAS). For example, some patients undergoing DBS for Parkinson’s Disease have developed hypersexuality, and some have reported increased apathy. Moreover, experimental psychiatric applications of DBS may intentionally seek to elicit changes to the patient’s dispositional, emotional and behavioural states, in so far as dysfunctions in these states may undergird the targeted disorder. Such changes following DBS have been of considerable interest to ethicists, but there is a considerable degree of conflict amongst different parties to this debate about whether DBS really does change PIAAAS, and whether this matters. This paper explores these conflicting views and suggests that we may be able to mediate this conflict by attending more closely to what parties to the debate mean when they invoke the concepts lumped together under the acronym PIAAAS. Drawing on empirical work on patient attitudes, this paper outlines how these different understandings of the concepts incorporated into PIAAAS have been understood in this debate, and how they may relate to other fundamental concepts in medical ethics such as well-being and autonomy. The paper clarifies some key areas of disagreement in this context, and develops proposals for how ethicists might fruitfully contribute to future empirical assessments of apparent changes to PIAAAS following DBS treatment.
Technologies controlled directly by the brain are being developed, evolving based on insights gained from neuroscience, and rehabilitative medicine. Besides neuro-controlled prosthetics aimed at restoring function lost somehow, technologies controlled via brain-computer interfaces (BCIs) may also extend a user’s horizon of action, freed from the need for bodily movement. Whilst BCI-mediated action ought to be, on the whole, treated as conventional action, law and policy ought to be amended to accommodate BCI action by broadening the definition of action as “willed bodily movement”. Moreover, there are some dimensions of BCI mediated action that are significantly different to conventional cases. These relate to control. Specifically, to limits in both controllability of BCIs via neural states, and in foreseeability of outcomes from such actions. In some specific type of case, BCI-mediated action may be due to different ethical evaluation from conventional action.
Implantable brain-computer interfaces (BCIs) are being developed to restore speech capacity for those who are unable to speak. Patients with locked-in syndrome or amyotrophic lateral sclerosis could be able to use covert speech – vividly imagining saying something without actual vocalisation – to trigger neural controlled systems capable of synthesising speech. User control has been identified as particularly pressing for this type of BCI. The incorporation of machine learning and statistical language models into the decoding process introduces a contribution to (or ‘shaping of’) the output that is beyond the user’s control. Whilst this type of ‘shared control’ of BCI action is not unique to speech BCIs, the automated shaping of what a user ‘says’ has a particularly acute ethical dimension, which may differ from parallel concerns surrounding automation in movement BCIs. This paper provides an analysis of the control afforded to the user of a speech BCI of the sort under development, as well as the relationships between accuracy, control, and the user’s ownership of the speech produced. Through comparing speech BCIs with BCIs for movement, we argue that, whilst goal selection is the more significant locus of control for the user of a movement BCI, control over process will be more significant for the user of the speech BCI. The design of the speech BCI may therefore have to trade off some possible efficiency gains afforded by automation in order to preserve sufficient guidance control necessary for users to express themselves in ways they prefer. We consider the implications for the speech BCI user’s responsibility for produced outputs and their ownership of token outputs. We argue that these are distinct assessments. Ownership of synthetic speech concerns whether the content of the output sufficiently represents the user, rather than their morally relevant, causal role in producing that output.
This article presents the first thematic review of the literature on the ethical issues concerning digital well-being. The term ‘digital well-being’ is used to refer to the impact of digital technologies on what it means to live a life that is good for a human being. The review explores the existing literature on the ethics of digital well-being, with the goal of mapping the current debate and identifying open questions for future research. The review identifies major issues related to several key social domains: healthcare, education, governance and social development, and media and entertainment. It also highlights three broader themes: positive computing, personalised human–computer interaction, and autonomy and self-determination. The review argues that three themes will be central to ongoing discussions and research by showing how they can be used to identify open questions related to the ethics of digital well-being.
One important concern regarding implantable Brain Computer Interfaces (BCI) is the fear that the intervention will negatively change a patient’s sense of identity or agency. In particular, there is concern that the user will be psychologically worse-off following treatment despite postoperative functional improvements. Clinical observations from similar implantable brain technologies, such as deep brain stimulation (DBS), show a small but significant proportion of patients report feelings of strangeness or difficulty adjusting to a new concept of themselves characterized by a maladaptive je ne sais quoi despite clear motor improvement. Despite the growing number of cases in the DBS literature, there is currently no accepted or standardized tool in neuroethics specifically designed to capture the phenomenological postoperative experience of patients implanted with DBS or BCI devices. Given potential risks of postoperative maladaptation, it is important for the field of neuroethics to develop a qualitative instrument that can serve as a shared method for capturing postoperative variations in patient experience of identity and agency. The goal of this article is to introduce an instrument we have developed for this purpose and call for further neuroethical efforts to assess the phenomenology of implantable brain device use.
Advanced brain machine interfaces provide potentially transformative approaches to treating neurological conditions and enhancing the performance of users. Yet, as technological capabilities continue to progress in leaps and bounds, there is a possibility that these capabilities outstrip our collective understanding of how to ensure brain machine interfaces are developed and used ethically and responsibly. In this case, there is an overt danger of rapid technological developments leading to unanticipated harm through a lack of foresight including threats to privacy, autonomy, self-identity, and other areas of personal and social value which, while hard to quantify, represent substantial risks. There is also a very real likelihood of such risks undermining value creation around the technologies and the associated enterprises, as key stakeholders push back against perceived and actual threats to what they, in turn, hold to be of value. In order to successfully traverse the resulting risk landscape, researchers and developers will need to become increasingly adept at integrating a sophisticated understanding of ethical and socially responsible innovation into their enterprises. Here, we illustrate how a "risk innovation" approach may provide novel insights into mapping out this landscape and revealing potentially blindsiding risks. We show how this approach can be used to illuminate challenges and opportunities to the successful, ethical, and responsible development of advanced brain machine interfaces. In addition, we emphasize how success will ultimately depend on the willingness of innovators and others to take ethical and responsible innovation seriously and to draw on the interdisciplinary and transdisciplinary expertise that is necessary to translate good intentions into positive outcomes.
The past two decades have seen unprecedented progress in the development of novel materials, form factors, and functionalities in neuroimplantable technologies, including electrocorticography (ECoG) systems, multielectrode arrays (MEAs), Stentrode, and deep brain probes. The key considerations for the development of such devices intended for acute implantation and chronic use, from the perspective of biocompatible hybrid materials incorporation, conformable device design, implantation procedures, and mechanical and biological risk factors, are highlighted. These topics are connected with the role that the U.S. Food and Drug Administration (FDA) plays in its regulation of neuroimplantable technologies based on the above parameters. Existing neuroimplantable devices and efforts to improve their materials and implantation protocols are first discussed in detail. The effects of device implantation with regards to biocompatibility and brain heterogeneity are then explored. Topics examined include brain‐specific risk factors, such as bacterial infection, tissue scarring, inflammation, and vasculature damage, as well as efforts to manage these dangers through emerging hybrid, bioelectronic device architectures. The current challenges of gaining clinical approval by the FDA—in particular, with regards to biological, mechanical, and materials risk factors—are summarized. The available regulatory pathways to accelerate next‐generation neuroimplantable devices to market are then discussed. Recent progress in the development of existing neuroimplantable devices such as electrocorticography (ECoG) systems, multielectrode arrays (MEAs), Stentrode, and deep brain probes is reviewed. Furthermore, the materials science and regulatory perspective on such devices, with regards to their relationship with biological, mechanical, and material risk factors, are highlighted.
Darknet markets provide an anonymous, online platform for users to trade illicit drugs, fraudulent identity data, and other commodities. Although law enforcement agencies have been successful in seizing many markets, the Darknet is an agile and dynamic environment and market activities often persist and emerge in a new form. Given this constantly changing environment, new ways of disrupting darknet markets are required. This study used Event Analysis of Systemic Teamwork (EAST) to analyse market activity and understand vulnerabilities to disruption. This involved using the EAST broken-links approach to assess the effects of compromising the transmission of information between tasks and between agents. The analysis identified critical vulnerabilities in the system, which included information involved in registering, depositing funds, communicating listing details to buyers, and communicating dispute resolution messages. This study indicates that systems ergonomics methods—in particular, EAST—can provide insight into system vulnerabilities that might be targeted for disruption.
Practitioner Summary: This study provides a conceptualisation of the processes, people, structures, and information involved in the buying and selling of goods on a darknet market. Law enforcement agencies may use broken-links analyses to systematically consider the effects of their interventions.
We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.
Ethical issues concerning brain–computer interfaces (BCIs) have already received a considerable amount of attention. However, one particular form of BCI has not received the attention that it deserves: Affective BCIs that allow for the detection and stimulation of affective states. This paper brings the ethical issues of affective BCIs in sharper focus. The paper briefly reviews recent applications of affective BCIs and considers ethical issues that arise from these applications. Ethical issues that affective BCIs share with other neurotechnologies are presented and ethical concerns that are specific to affective BCIs are identified and discussed.
Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.
A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.
We revisit the side-channel attacks with brain–computer interfaces (BCIs) first put forward by Martinovic et al. at the USENIX 2012 Security Symposium. For this purpose, we propose a comprehensive investigation of concrete adversaries trying to extract a PIN code from electroencephalogram signals. Overall, our results confirm the possibility of partial PIN recovery with high probability of success in a more quantified manner and at the same time put forward the challenges of full/systematic PIN recovery. They also highlight that the attack complexities can significantly vary in function of the adversarial capabilities (e.g., supervised/profiled vs. unsupervised/non-profiled), hence leading to an interesting trade-off between their efficiency and practical relevance. We then show that similar attack techniques can be used to threat the privacy of BCI users. We finally use our experiments to discuss the impact of such attacks for the security and privacy of BCI applications at large, and the important emerging societal challenges they raise.
The field of neuroethics has had to adapt rapidly in the face of accelerating technological advancement; a particularly striking example is the realm of Brain-Computer Interface (BCI). A significant source of funding for the development of new BCI technologies has been the United States Department of Defense, and while the predominant focus has been restoration of lost function for those wounded in battle, there is also significant interest in augmentation of function to increase survivability, coordination, and lethality of US combat forces. While restoration of primary motor and sensory function (primary BCI) has been the main focus of research, there has been marked progress in interface with areas of the brain subserving memory and association. Non-Primary BCI has a different subset of potential applications, each of which also carries its own ethical considerations. Given the amount of BCI research funding coming from the Department of Defense, it is particularly important that potential military applications be examined from a neuroethical standpoint.
Restoring communication for people with locked-in syndrome remains a challenging clinical problem without a reliable solution. Recent studies have shown that people with paralysis can use brain-computer interfaces (BCIs) based on intracortical spiking activity to efficiently type messages. However, due to neuronal signal instability, most intracortical BCIs have required frequent calibration and continuous assistance of skilled engineers to maintain performance. Here, an individual with locked-in syndrome due to brain stem stroke and an individual with tetraplegia secondary to amyotrophic lateral sclerosis (ALS) used a simple communication BCI based on intracortical local field potentials (LFPs) for 76 and 138 days, respectively, without recalibration and without significant loss of performance. BCI spelling rates of 3.07 and 6.88 correct characters/minute allowed the participants to type messages and write emails. Our results indicate that people with locked-in syndrome could soon use a slow but reliable LFP-based BCI for everyday communication without ongoing intervention from a technician or caregiver.
NEW & NOTEWORTHY This study demonstrates, for the first time, stable repeated use of an intracortical brain-computer interface by people with tetraplegia over up to four and a half months. The approach uses local field potentials (LFPs), signals that may be more stable than neuronal action potentials, to decode participants’ commands. Throughout the several months of evaluation, the decoder remained unchanged; thus no technical interventions were required to maintain consistent brain-computer interface operation.
I use a hypothetical case study of a woman who replaces here biological arms with prostheses controlled through a brain–computer interface the explore how a BCI might interpret and misinterpret intentions. I define pre-veto intentions and post-veto intentions and argue that a failure of a BCI to differentiate between the two could lead to some troubling legal and ethical problems.
While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from the first-in-human clinical trial to study patients with implanted BCI advisory devices. We explored perceptions of self-change across six patients who volunteered to be implanted with artificially intelligent BCI devices. We used qualitative methodological tools grounded in phenomenology to conduct in-depth, semi-structured interviews. Results show that, on the one hand, BCIs can positively increase a sense of the self and control; on the other hand, they can induce radical distress, feelings of loss of control, and a rupture of patient identity. We conclude by offering suggestions for the proactive creation of preparedness protocols specific to intelligent—predictive and advisory—BCI technologies essential to prevent potential iatrogenic harms.
Accidents are a systems phenomenon and multiple methods are available to enable retrospective analysis of accidents through this lens. However, the same cannot be said for the methods available for forecasting risk and accidents. This paper describes a new systems-based risk assessment method, the NETworked hazard analysis and risk management system (NET-HARMS), that was designed to support practitioners in identifying (1) risks across overall work systems, and (2) emergent risks that are created when risks across the system interact with one another. An overview of NET-HARMS is provided and demonstrated through a case study application. An initial test of the method is provided by comparing case study outcomes (i.e. predicted risks) with accident data (i.e. actual risks) from the domain in question. Findings show that NET-HARMS is capable of forecasting systemic and emergent risks and that it could identify almost all risks that featured in the accidents in the comparison data-set.
A new generation of implantable Brain Computer Interfaces (BCI) devices have been tested for the first time in a human clinical trial with significant success. These intelligent implants detect specific neuronal activity patterns, like an epileptic seizure, then provide information to help patients to respond to the upcoming neuronal events. By forecasting a seizure, the technology keeps patients in the decisional loop; the device gives control to patients on how to respond and decide on a therapeutic course ahead of time. Being kept in the decisional loop can positively increase patients quality of life; however, doing so does not come free of ethical concerns. There is currently a lack of evidence concerning the various impacts of closed-loop system BCIs on patients' decision-making processes, especially how being in the decisional loop impacts patients' sense of autonomy. This article addresses these gaps by providing data we obtained from a first-in-human clinical trial involving patients implanted with advisory brain devices. This manuscript explores ethical issues related to the risks involved with being kept in the decisional loop.
The objective of this study was to assess the feasibility of hybrid SSVEP + P300 visual BCI systems for quad-copter flight control in physical world. Existing BCI-based quad-copter flight control has limitations of slow navigation, lower system accuracy, rigorous user training requirement and lesser number of independent control commands. So, there is need of hybrid BCI design that combines evoked SSVEP and P300 potentials to control flight direction of quad-copter movement. GUI design is developed such that user can effectively control quad-copter flight by gazing at visual stimuli buttons that produce SSVEP & P300 potentials simultaneously in human cortex. We compare the performance metrics of the proposed flight control systems with other existing BCI-based flight control as conventional SSVEP BCI and P300 BCI and commercially available keyboard flight control systems. Results proved that the proposed system outperforms the existing BCI-based flight control systems but has slightly lower performance efficiency than the commercial keyboard flight control systems. Further, the proposed quad-copter flight control system proved its suitability for patients with severe motor disabilities.
Recently, the use of collaborative robots has started to emerge at construction sites. Such incorporation into these human-dominated environments can raise safety concerns as most robots are not fully automated and require some sort of control mechanism. Conventional control systems may fall short under specific situations in which workers require robotic assistance but cannot use their hands to control the robot. Brain-computer interface (BCI) can offer such hands-free controllability, a non-muscular communicative channel that can establish an interpretive pathway between humans and robots. This paper proposes a BCI-based system to remotely control a robot by continuously capturing workers' brainwaves acquired from a wearable electroencephalogram (EEG) device and interpreting them into robotic commands with 90% accuracy. The findings revealed the proposed system holds promise for enhancing robot control in hazardous operations where the ability of the worker to physically direct the robot is limited, such as underwater and space construction.
Résumé
L’interface cerveau-machine (ICM) est une technique permettant de recueillir l’activité du cortex et de la transformer, grâce à un logiciel, en commande d’activités motrices ou sensorielles définies respectivement comme asynchrone et synchrone parce que dans ce dernier cas le cortex est stimulé par un événement extérieur alors que dans le premier il enclenche par la pensée un ordre moteur indépendant des nerfs et des muscles. Le recueil de l’activité corticale est soit extracrânien, et utilise l’électroencéphalographie, soit intracrânien avec implantation d’électrodes dans le cortex recueillant les potentiels émis. Les applications médicales de l’ICM visent à restaurer la mobilité d’un ou plusieurs membres suite à un accident ou une maladie. L.A. Benabid a mis au point une technique de commande corticale d’un exosquelette pour traiter des patients paraplégiques. G. Courtine et J. Bloch proposent de réactiver des circuits moteurs lombaires épargnés lors de l’accident. N. Jarasse et G. Morel font appel à des patrons supports constitués à partir des mouvements du tronc et de l’épaule accompagnant le mouvement voulu de la prothèse du bras amputé. A. Lecuyer a développé un logiciel « OpenViBE » appliqué au « neurofeedback » qui permet au patient visualisant les ondes de son cerveau de les ramener par entraînement à une norme afin d’améliorer déficits moteurs et troubles mentaux. Pour restaurer la vision en cas de perte des photorécepteurs, S. Picaud applique une stratégie consistant à réintroduire des informations visuelles dans le circuit neuronal en utilisant une puce électronique implantée en épi ou sous-rétinien afin de stimuler les cellules ganglionnaires et atteindre in fine le cortex visuel. Toutes ces recherches concernent un nombre isolé de malades rarement étudiés dans des conditions de vie normale. Pour passer à l’étape industrielle, plusieurs défis doivent être affrontés : mieux connaître les réseaux neuronaux impliqués, développer la détection des signaux neuronaux avec des électrodes plus fines, plus résistantes et assemblées dans des matrices en en positionnant un très grand nombre à la surface du cortex, progresser dans le domaine de l’informatique et de l’analyse des données. Toutes ces techniques soulèvent des problèmes éthiques dont les 3 principaux sont : de préserver le caractère personnel de l’activité neuronale, de refuser toute coercition vis-à-vis du patient, et d’optimiser des dispositifs sauvegardant son pouvoir de décider. Ce rapport recommande un soutien public en faveur de la recherche académique et des start-ups, la création d’une structure de mise en réseau des unités de recherche travaillant dans le domaine, le refus d’une autocratie permise par le développement du numérique.
Neurotechnology is set to expand rapidly in the coming years as technological innovations in hardware and software are translated to the clinical setting. Given our unique access to patients with neurologic disorders, expertise with which to guide appropriate treatments, and technical skills to implant brain–machine interfaces (BMIs), neurosurgeons have a key role to play in the progress of this field.
We outline the current state and key challenges in this rapidly advancing field, including implant technology, implant recipients, implantation methodology, implant function, and ethical, regulatory, and economic considerations. Our key message is to encourage the neurosurgical community to proactively engage in collaborating with other health care professionals, engineers, scientists, ethicists, and regulators in tackling these issues. By doing so, we will equip ourselves with the skills and expertise to drive the field forward and avoid being mere technicians in an industry driven by those around us.
Brain-computer interfaces (BCIs) are systems in which a user's real-time brain activity is used to control an external device, such as a prosthetic limb. BCIs have great potential for restoring lost motor functions in a wide range of patients. However, this futuristic technology raises several ethical questions, especially concerning the degree of agency a BCI affords its user and the extent to which a BCI user ought to be accountable for actions undertaken via the device. This paper examines these and other ethical concerns found at each of the three major parts of the BCI system: the sensor that records neural activity, the decoder that converts raw data into usable signals, and the translator that uses these signals to control the movement of an external device.
Brain-computer interface (BCI) technology will usher in profound changes to the practice of medicine. BCI devices, broadly defined as those capable of reading brain activity and translating this into operation of a device, will offer patients and clinicians new ways to address impairments of communication, movement, sensation, and mental health. These new capabilities will bring new responsibilities and raise a diverse set of ethical challenges. One way to understand and begin to address these challenges is to view them in terms of the goals of medicine. In this chapter, different ways in which BCI technology may subserve the goals of medicine is explored. This is followed by articulation of additional goals particularly relevant to BCI technology: neural diversity, neural privacy, agency, and authenticity. The goals of medicine provide a useful ethical framework for the introduction of BCI devices into medicine.
Neuroprosthetic speech devices are an emerging technology that can offer the possibility of communication to those who are unable to speak. Patients with ‘locked in syndrome,’ aphasia, or other such pathologies can use covert speech—vividly imagining saying something without actual vocalization—to trigger neural controlled systems capable of synthesizing the speech they would have spoken, but for their impairment.
We provide an analysis of the mechanisms and outputs involved in speech mediated by neuroprosthetic devices. This analysis provides a framework for accounting for the ethical significance of accuracy, control, and pragmatic dimensions of prosthesis-mediated speech. We first examine what it means for the output of the device to be accurate, drawing a distinction between technical accuracy on the one hand and semantic accuracy on the other. These are conceptual notions of accuracy.
Both technical and semantic accuracy of the device will be necessary (but not yet sufficient) for the user to have sufficient control over the device. Sufficient control is an ethical consideration: we place high value on being able to express ourselves when we want and how we want. Sufficient control of a neural speech prosthesis requires that a speaker can reliably use their speech apparatus as they want to, and can expect their speech to authentically represent them. We draw a distinction between two relevant features which bear on the question of whether the user has sufficient control: voluntariness of the speech and the authenticity of the speech . These can come apart: the user might involuntarily produce an authentic output (perhaps revealing private thoughts) or might voluntarily produce an inauthentic output (e.g., when the output is not semantically accurate). Finally, we consider the role of the interlocutor in interpreting the content and purpose of the communication.
These three ethical dimensions raise philosophical questions about the nature of speech, the level of control required for communicative accuracy, and the nature of ‘accuracy’ with respect to both natural and prosthesis-mediated speech.
As technologies that integrate the brain with computers become more complex, so too do the ethical issues that surround their use. As technologies that integrate the brain with computers become more complex, so too do the ethical issues that surround their use.
Brain-Computer Interfaces (BCIs) are specialized systems that allow users to control computer applications using their brain waves. With the advent of consumer-grade electroencephalography (EEG) devices, brain-controlled systems started to find applications outside of the medical field, opening many research opportunities in the area of Human-Computer Interaction (HCI). One particular area that is gaining more evidence due to the arrival of consumer-grade devices is that of computer games, as it allows more user-friendly applications of BCI technology for the general public. In this paper, the results of a Systematic Literature Review (SLR) of BCI games using consumer-grade devices are presented. Papers published in a time span of 12 years were reviewed and their data collected using a rigid systematic process. Several analyses were made based on the gathered data, and a clear view of the current scenario and challenges for HCI of BCI-based games using consumer-grade devices is provided. The search shows that although many games were created with simplified controls for research purposes, there was an increasing number of more user-friendly BCI games, especially for entertainment. The most predominant control signals were the attention and meditation, followed by motor imagery and emotion recognition, being mainly captured by NeuroSky and Emotiv EEG devices. The results also show that there are still many open issues and research opportunities in the field of HCI for BCI-based games, as most evaluations investigated only quantitative aspects of the BCI systems, while very few studies analyzed usability and qualitative aspects of the users' interaction with the games.
In this study, a dynamic user interface (UI) is proposed in visual P300 Brain-Computer Interface (BCI) based environmental control system. A head-mounted Augmented Reality (AR) glass is used as the interactive media, which is used to assists the BCI system to build the dynamic UI with the scene in subject’s field of view. In the dynamic UI, based on the objects detected by the AR glass, options are dynamically generated. The subject can assign tasks by selecting different options in the dynamic UI. Five subjects successfully completed the task of controlling household appliances and navigating wheelchairs to designated destinations. Compared to static UI, the proposed dynamic UI has a 17.4% improvement in time delay. On average, only 1.9% of the commands resulted in incorrect operations. The dynamic UI makes progress in reducing time delay and incorrect operations. The proposed system provides a brand-new interactive method in BCI based applications.
Background
Technologies such as brain‐computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy.
Objective
The aim of this systematic review was to provide a status report regarding advances in brain‐computer interface, focusing in particular in upper limb motor recovery.
Methods
The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain‐computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale.
Results
From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non‐invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl–Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies.
Conclusions
Neurofeedback training with brain‐computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long‐term efficacy.
Brain–computer interfaces (BCIs) are driven essentially by algorithms; however, the ethical role of such algorithms has so far been neglected in the ethical assessment of BCIs. The goal of this article is therefore twofold: First, it aims to offer insights into whether (and how) the problems related to the ethics of BCIs (e.g., responsibility) can be better grasped with the help of already existing work on the ethics of algorithms. As a second goal, the article explores what kinds of solutions are available in that body of scholarship, and how these solutions relate to some of the ethical questions around BCIs. In short, the article asks what lessons can be learned about the ethics of BCIs from looking at the ethics of algorithms. To achieve these goals, the article proceeds as follows. First, a brief introduction into the algorithmic background of BCIs is given. Second, the debate about epistemic concerns and the ethics of algorithms is sketched. Finally, this debate is transferred to the ethics of BCIs.
A light aircraft crosswind takeoff is a risky operation. The purpose of this paper is to demonstrate the feasibility of applying STPA (Systems-Theoretic Process Analysis) to closed-loop continuous controls, identifying the hazards of crosswind takeoffs with light aircraft and the mitigating actions that could make its execution safer. The paper analyzes the variables that affect the response of the aircraft when subjected to severe crosswind, considering how aircraft characteristics affect its stability. The hazard analysis technique STPA is a tool based on the conceptual accident causality model called STAMP (System-Theoretic Accident Model and Processes), which in turn is based on systems theory. To deal with closed-loop actions on continuous control systems, a new approach to STPA was developed and effectively used to analyze data collected on a crosswind flight test campaign. This campaign, conducted by the Flight Test and Research Institute, led to a flight envelope extension of the Embraer’s training aircraft Super Tucano. The demonstration analysis showed the need for new, previously unidentified mitigating measures to be assigned to aircraft manufacturers, operators or owners, and their pilots.
Brain-computer interfaces (BCIs) not only can allow individuals to voluntarily control external devices, helping to restore lost motor functions of the disabled, but can also be used by healthy users for entertainment and gaming applications. In this study, we proposed a hybrid BCI paradigm to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. In this paradigm, we combined motor imagery (MI) and steady-state visually evoked potentials (SSVEPs) to generate multiple commands. A classic game, Tetris, was chosen as the control object. The novelty of this study includes the effective usage of a “dwell time” approach and fusion rules to design BCI games. To demonstrate the feasibility of the proposed hybrid paradigm, ten subjects were chosen to participate in online control experiments. The experimental results showed that all subjects successfully completed the predefined tasks with high accuracy. This proposed hybrid BCI paradigm could potentially provide those who suffer disability or paralysis with additional entertainment options, such as brain-actuated games, that could improve their happiness and quality of life.
Abbreviations: BCI: brain-computer interface; EEG: electroencephalogram; MI: motor imagery; SSVEP: steady-state visually evoked potential; ERP: event-related potential; SMR: sensorimotor rhythm; VEP: visual evoked potential; TCP/IP: transmission control protocol/internet protocol; GUI: graphical user interface; ERD/ERS: event-related desynchronization/synchronization; CIC: control intention classifier; LRC: left/right classifier; CSP: common spatial pattern; LDA: linear discriminant analysis; ROC: receiver operating characteristic; TPR: true positive rate; FPR: false positive rate; CCA: canonical correlation analysis.
Wireless powered implants, each smaller than a grain of rice, have the potential to scan and stimulate brain cells. Further research may lead to next-generation brain-machine interfaces for controlling prosthetics, exoskeletons, and robots, as well as “electroceuticals” to treat disorders of the brain and body. In conditions that can be particularly alleviated with brain stimulation, the use of such mini devices may pose certain challenges. Health professionals are becoming increasingly more accountable in decision-making processes that have impacts on the life quality of individuals. It is possible to transmit such stimulation using remote control principles. Perhaps, the most important concern regarding the use of these devices termed as “neural dust” is represented by the possibility of controlling affection and other mental functions via waves reaching the brain using more advanced versions of such devices. This will not only violate the respect for authority principle of ethics, but also medical ethics, and may potentially lead to certain incidents of varying vehemence that may be considered illegal. Therefore, a sound knowledge and implementation of ethical principles is becoming a more important issue on the part of healthcare professionals. In both the ethical decision-making process and in ethical conflicts, it may be useful to re-appraise the principles of medical ethics. In this article, the ethical considerations of these devices are discussed.