scholarly journals Ethical Considerations in Ending Exploratory Brain–Computer Interface Research Studies in Locked-in Syndrome

2018 ◽  
Vol 27 (4) ◽  
pp. 660-674 ◽  
Author(s):  
ERAN KLEIN ◽  
BETTS PETERS ◽  
MATT HIGGER
Keyword(s):  
Randomized Clinical Trials ◽  
Ethical Issues ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Ethical Challenges ◽  
Ethical Considerations ◽  
Ethical Frameworks ◽  
Recent Attention ◽  
Potential Tool

Abstract:Brain–computer interface (BCI) is a promising technology for restoring communication in individuals with locked-in syndrome (LIS). BCI technology offers a potential tool for individuals with impaired or absent means of effective communication to use brain activity to control an output device such as a computer keyboard. Exploratory studies of BCI devices for communication in people with LIS are underway. Research with individuals with LIS presents not only technological challenges, but ethical challenges as well. Whereas recent attention has been focused on ethical issues that arise at the initiation of studies, such as how to obtain valid consent, relatively little attention has been given to issues at the conclusion of studies. BCI research in LIS highlights one such challenge: How to decide when an exploratory BCI research study should end. In this article, we present the case of an individual with presumed LIS enrolled in an exploratory BCI study. We consider whether two common ethical frameworks for stopping randomized clinical trials—equipoise and nonexploitation—can be usefully applied to elucidating researcher obligations to end exploratory BCI research. We argue that neither framework is a good fit for exploratory BCI research. Instead, we apply recent work on clinician-researcher fiduciary obligations and in turn offer some preliminary recommendations for BCI researchers on how to end exploratory BCI studies.

Do Publics Share Experts’ Concerns about Brain–Computer Interfaces? A Trinational Survey on the Ethics of Neural Technology

2019 ◽  
Vol 45 (6) ◽  
pp. 1242-1270 ◽  
Author(s):  
Matthew Sample ◽  
Sebastian Sattler ◽  
Stefanie Blain-Moraes ◽  
David Rodríguez-Arias ◽  
Eric Racine
Keyword(s):  
Healthcare Professionals ◽  
Ethical Issues ◽  
Brain Activity ◽  
Public Attitudes ◽  
New Technology ◽  
Computer Interface ◽  
Academic Ethics ◽  
Ethical Challenges ◽  
Computer Interfaces ◽  
The 1960S

Since the 1960s, scientists, engineers, and healthcare professionals have developed brain–computer interface (BCI) technologies, connecting the user’s brain activity to communication or motor devices. This new technology has also captured the imagination of publics, industry, and ethicists. Academic ethics has highlighted the ethical challenges of BCIs, although these conclusions often rely on speculative or conceptual methods rather than empirical evidence or public engagement. From a social science or empirical ethics perspective, this tendency could be considered problematic and even technocratic because of its disconnect from publics. In response, our trinational survey (Germany, Canada, and Spain) reports public attitudes toward BCIs ( N = 1,403) on ethical issues that were carefully derived from academic ethics literature. The results show moderately high levels of concern toward agent-related issues (e.g., changing the user’s self) and consequence-related issues (e.g., new forms of hacking). Both facets of concern were higher among respondents who reported as female or as religious, while education, age, own and peer disability, and country of residence were associated with either agent-related or consequence-related concerns. These findings provide a first look at BCI attitudes across three national contexts, suggesting that the language and content of academic BCI ethics may resonate with some publics and their values.

Pandemic Disease, Public Health, and Ethics

2019 ◽  
pp. 796-811 ◽  
Author(s):  
Maxwell Smith ◽  
Ross Upshur
Keyword(s):  
Public Health ◽  
Ethical Issues ◽  
Ethical Challenges ◽  
Pandemic Preparedness ◽  
Scarce Resources ◽  
Ethical Frameworks ◽  
Health Measures ◽  
Care Workers ◽  
The Face ◽  
Conducting Research

Infectious disease pandemics raise significant and novel ethical challenges to the organization and practice of public health. This chapter provides an overview of the salient ethical issues involved in preparing for and responding to pandemic disease, including those arising from deploying restrictive public health measures to contain and curb the spread of disease (e.g., isolation and quarantine), setting priorities for the allocation of scarce resources, health care workers’ duty to care in the face of heightened risk of infection, conducting research during pandemics, and the global governance of preventing and responding to pandemic disease. It also outlines ethical guidance from prominent ethical frameworks that have been developed to address these ethical issues and concludes by discussing some pressing challenges that must be addressed if ethical reflection is to make a meaningful difference in pandemic preparedness and response.

The Ethics of AI in Biomedical Research, Patient Care, and Public Health

2020 ◽  
pp. 702-718 ◽  
Author(s):  
Alessandro Blasimme ◽  
Effy Vayena
Keyword(s):  
Public Health ◽  
Patient Care ◽  
Biomedical Research ◽  
Disease Surveillance ◽  
Ethical Issues ◽  
Ethical Review ◽  
Ethical Challenges ◽  
Healthcare Provision ◽  
Social Scientists ◽  
Ethical Frameworks

This chapter explores ethical issues raised by the use of artificial intelligence (AI) in the domain of biomedical research, healthcare provision, and public health. The litany of ethical challenges that AI in medicine raises cannot be addressed sufficiently by current regulatory and ethical frameworks. The chapter then advances the systemic oversight approach as a governance blueprint, which is based on six principles offering guidance as to the desirable features of oversight structures and processes in the domain of data-intense biomedicine: adaptivity, flexibility, inclusiveness, reflexivity, responsiveness, and monitoring (AFIRRM). In the research domain, ethical review committees will have to incorporate reflexive assessment of the scientific and social merits of AI-driven research and, as a consequence, will have to open their ranks to new professional figures such as social scientists. In the domain of patient care, clinical validation is a crucial issue. Hospitals could equip themselves with “clinical AI oversight bodies” charged with the task of advising clinical administrators. Meanwhile, in the public health sphere, the new level of granularity enabled by AI in disease surveillance or health promotion will have to be negotiated at the level of targeted communities.

scholarly journals Effortless Retaliation: the Neural Dynamics of Interpersonal Intentions in a Chicken Game Using Brain-Computer Interface

2021 ◽  
Author(s):  
Yiwen Wang ◽  
Yuxiao Lin ◽  
Chao Fu ◽  
Zhihua Huang ◽  
Rongjun Yu ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Neural Mechanisms ◽  
Computer Interface ◽  
Neural Dynamics ◽  
Neural Signals ◽  
Chicken Game ◽  
Common Response ◽  
Tit For Tat ◽  
Novel Method

Abstract The desire for retaliation is a common response across a majority of human societies. However, the neural mechanisms underlying aggression and retaliation remain unclear. Previous studies on social intentions are confounded by low-level response related brain activity. Using an EEG-based brain-computer interface (BCI) combined with the Chicken Game, our study examined the neural dynamics of aggression and retaliation after controlling for nonessential response related neural signals. Our results show that aggression is associated with reduced alpha event-related desynchronization (ERD), indicating reduced mental effort. Moreover, retaliation and tit-for-tat strategy use are also linked with smaller alpha-ERD. Our study provides a novel method to minimize motor confounds and demonstrates that choosing aggression and retaliation is less effortful in social conflicts.

scholarly journals Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

2019 ◽  
Author(s):  
Jennifer Stiso ◽  
Marie-Constance Corsi ◽  
Javier Omar Garcia ◽  
Jean M Vettel ◽  
Fabrizio De Vico Fallani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Distributed Networks ◽  
Motor Dysfunction ◽  
Computer Interface ◽  
Specific Method ◽  
Temporal Expression ◽  
And Behavior ◽  
Brain Behavior

Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method -- non-negative matrix factorization -- to simultaneously identify regularized, covarying subgraphs of functional connectivity and behavior, and to detect the time-varying expression of each subgraph. We find that learning is marked by distributed brain-behavior relations: swifter learners displayed many subgraphs whose temporal expression tracked performance. Learners also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in networks important for sustaining attention. After formalizing the model in the framework of network control theory, we test the model and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

scholarly journals Could Brain–Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?

2021 ◽  
Vol 15 ◽  
Author(s):  
Stuti Chakraborty ◽  
Gianluca Saetta ◽  
Colin Simon ◽  
Bigna Lenggenhager ◽  
Kathy Ruddy
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Treatment Options ◽  
Body Representation ◽  
Vestibular Stimulation ◽  
Current Treatment ◽  
Substantial Improvement ◽  
Computer Interface ◽  
Effective Form ◽  
Brain Correlates

Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person’s anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain–computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.

Ethical issues in social marketing

2017 ◽  
Author(s):  
Lynne Eagle ◽  
Stephan Dahl ◽  
David Low
Keyword(s):  
Social Marketing ◽  
Financial Incentives ◽  
Potential Role ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Ethical Issues ◽  
Code Of Ethics ◽  
Ethical Challenges ◽  
Ethical Frameworks

Many social marketers assume that because they are focusing on positive behaviour change they may expect fewer ethical issues arising from their work than conventional, commercial marketing. However, such a view is sadly too simplistic. This chapter focuses on the ethical issues facing social marketing. It argues that social marketers face an even greater potential for ethical issues and gives examples of these by focusing on targeting, stigmatization, victim blaming, coercion, and the use of financial incentives, among others. Recognizing the manifold potential for ethical challenges, and the lack of a decisive manner in which to resolve them easily, it then discusses ethical frameworks which can aid social marketers in formulating a response to potential issues and arriving at a considered decision. The chapter concludes by discussing the potential role of a professional code of ethics, and how this can aid future ethical decision-making.

scholarly journals Brain-Computer Interfaces for Internet of Things

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1179 ◽  
Author(s):  
Francisco Laport ◽  
Francisco J. Vazquez-Araujo ◽  
Paula M. Castro ◽  
Adriana Dapena
Keyword(s):  
Internet Of Things ◽  
Communication Protocol ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Event Classification ◽  
Computer Interfaces ◽  
Controlling Elements ◽  
Artefact Reduction ◽  
The Brain

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

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