Asilomar survey: researcher perspectives on ethical principles and guidelines for BCI research

Factors influencing the social acceptance of brain-computer interface technology among Chinese general public: an exploratory study

This study investigates the impact of social factors on public acceptance of brain-computer interface (BCI) technology within China's general population. As BCI emerges as a pivotal advancement in artificial intelligence and a cornerstone of Industry 5.0, understanding its societal reception is crucial. Utilizing data from the Psychological and Behavioral Study of Chinese Residents (N = 1,923), this research examines the roles of learning ability, age, health, social support, and socioeconomic status in BCI acceptance, alongside considerations of gender and the level of monthly household income. Multiple regression analysis via STATA-MP18 reveals that while health, socioeconomic status, social support, and learning ability significantly positively correlate with acceptance, and age presents an inverse relationship, gender and household income do not demonstrate a significant effect. Notably, the prominence of learning ability and social support as principal factors suggests targeted avenues for increasing BCI technology adoption. These findings refine the current understanding of technology acceptance and offer actionable insights for BCI policy and practical applications.

Post-trial access to implantable neural devices: an exploratory international survey

Objectives

Clinical trials of innovative neural implants are rapidly increasing and diversifying, but little is known about participants' post-trial access to the device and ongoing clinical care. This exploratory study examines common practices in the planning and coordination of post-trial access to neurosurgical devices. We also explore the perspectives of trial investigators on the barriers to post-trial access and ongoing care, as well as ethical questions related to the responsibilities of key stakeholder groups.

Design setting and participants

Trial investigators (n=66) completed a survey on post-trial access in the most recent investigational trial of a surgically implanted neural device they had conducted. Survey respondents predominantly specialized in neurosurgery, neurology and psychiatry, with a mean of 14.8 years of experience working with implantable neural devices.

Main outcome measures

Outcomes of interest included rates of device explantation during or at the conclusion of the trial (pre-follow-up) and whether plans for post-trial access were described in the study protocol. Outcomes also included investigators' greatest 'barrier' and 'facilitator' to providing research participants with post-trial access to functional implants and perspectives on current arrangements for the sharing of post-trial responsibilities among key stakeholders.

Results

Trial investigators reported either 'all' (64%) or 'most' (33%) trial participants had remained implanted after the end of the trial, with 'infection' and 'non-response' the most common reasons for explantation. When asked to describe the main barriers to facilitating post-trial access, investigators described limited funding, scarcity of expertise and specialist clinical infrastructure and difficulties maintaining stakeholder relationships. Notwithstanding these barriers, investigators overwhelmingly (95%) agreed there is an ethical obligation to provide post-trial access when participants individually benefit during the trial.

Conclusions

On occasions when devices were explanted during or at the end of the trial, this was done out of concern for the safety and well-being of participants. Further research into common practices in the post-trial phase is needed and essential to ethical and pragmatic discussions regarding stakeholder responsibilities.

Developer perspectives on the ethics of AI-driven neural implants: a qualitative study

Convergence of neural implants with artificial intelligence (AI) presents opportunities for the development of novel neural implants and improvement of existing neurotechnologies. While such technological innovation carries great promise for the restoration of neurological functions, they also raise ethical challenges. Developers of AI-driven neural implants possess valuable knowledge on the possibilities, limitations and challenges raised by these innovations; yet their perspectives are underrepresented in academic literature. This study aims to explore perspectives of developers of neurotechnology to outline ethical implications of three AI-driven neural implants: a cochlear implant, a visual neural implant, and a motor intention decoding speech-brain-computer-interface. We conducted semi-structured focus groups with developers (n = 19) of AI-driven neural implants. Respondents shared ethically relevant considerations about AI-driven neural implants that we clustered into three themes: (1) design aspects; (2) challenges in clinical trials; (3) impact on users and society. Developers considered accuracy and reliability of AI-driven neural implants conditional for users' safety, authenticity, and mental privacy. These needs were magnified by the convergence with AI. Yet, the need for accuracy and reliability may also conflict with potential benefits of AI in terms of efficiency and complex data interpretation. We discuss strategies to mitigate these challenges.

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

PURPOSE

To examine the views of individuals with neurodegenerative diseases about ethical issues related to incorporating personalized language models into brain-computer interface (BCI) communication technologies.

METHODS

Fifteen semi-structured interviews and 51 online free response surveys were completed with individuals diagnosed with neurodegenerative disease that could lead to loss of speech and motor skills. Each participant responded to questions after six hypothetical ethics vignettes were presented that address the possibility of building language models with personal words and phrases in BCI communication technologies. Data were analyzed with consensus coding, using modified grounded theory.

RESULTS

Four themes were identified. (1) The experience of a neurodegenerative disease shapes preferences for personalized language models. (2) An individual's identity will be affected by the ability to personalize the language model. (3) The motivation for personalization is tied to how relationships can be helped or harmed. (4) Privacy is important to people who may need BCI communication technologies. Responses suggest that the inclusion of personal lexica raises ethical issues. Stakeholders want their values to be considered during development of BCI communication technologies.

CONCLUSIONS

With the rapid development of BCI communication technologies, it is critical to incorporate feedback from individuals regarding their ethical concerns about the storage and use of personalized language models. Stakeholder values and preferences about disability, privacy, identity and relationships should drive design, innovation and implementation.IMPLICATIONS FOR REHABILITATIONIndividuals with neurodegenerative diseases are important stakeholders to consider in development of natural language processing within brain-computer interface (BCI) communication technologies.The incorporation of personalized language models raises issues related to disability, identity, relationships, and privacy.People who may one day rely on BCI communication technologies care not just about usability of communication technology but about technology that supports their values and priorities.Qualitative ethics-focused research is a valuable tool for exploring stakeholder perspectives on new capabilities of BCI communication technologies, such as the storage and use of personalized language models.

Sensor Technology and Intelligent Systems in Anorexia Nervosa: Providing Smarter Healthcare Delivery Systems

Ubiquitous technology, big data, more efficient electronic health records, and predictive analytics are now at the core of smart healthcare systems supported by artificial intelligence. In the present narrative review, we focus on sensing technologies for the healthcare of Anorexia Nervosa (AN). We employed a framework inspired by the Interpersonal Neurobiology Theory (IPNB), which posits that human experience is characterized by a flow of energy and information both within us (within our whole body), and between us (in the connections we have with others and with nature). In line with this framework, we focused on sensors designed to evaluate bodily processes (body sensors such as implantable sensors, epidermal sensors, and wearable and portable sensors), human social interaction (sociometric sensors), and the physical environment (indoor and outdoor ambient sensors). There is a myriad of man-made sensors as well as nature-based sensors such as plants that can be used to design and deploy intelligent systems for human monitoring and healthcare. In conclusion, sensing technologies and intelligent systems can be employed for smarter healthcare of AN and help to relieve the burden of health professionals. However, there are technical, ethical, and environmental sustainability issues that must be considered prior to implementing these systems. A joint collaboration of professionals and other members of the society involved in the healthcare of individuals with AN can help in the development of these systems. The evolution of cyberphysical systems should also be considered in these collaborations.

The ethics of AI-assisted warfighter enhancement research and experimentation: Historical perspectives and ethical challenges

The military applications of AI raise myriad ethical challenges. Critical among them is how AI integrates with human decision making to enhance cognitive performance on the battlefield. AI applications range from augmented reality devices to assist learning and improve training to implantable Brain-Computer Interfaces (BCI) to create bionic "super soldiers." As these technologies mature, AI-wired warfighters face potential affronts to cognitive liberty, psychological and physiological health risks and obstacles to integrating into military and civil society during their service and upon discharge. Before coming online and operational, however, AI-assisted technologies and neural interfaces require extensive research and human experimentation. Each endeavor raises additional ethical concerns that have been historically ignored thereby leaving military and medical scientists without a cogent ethics protocol for sustainable research. In this way, this paper is a "prequel" to the current debate over enhancement which largely considers neuro-technologies once they are already out the door and operational. To lay the ethics foundation for AI-assisted warfighter enhancement research, we present an historical overview of its technological development followed by a presentation of salient ethics research issues (ICRC, 2006). We begin with a historical survey of AI neuro-enhancement research highlighting the ethics lacunae of its development. We demonstrate the unique ethical problems posed by the convergence of several technologies in the military research setting. Then we address these deficiencies by emphasizing how AI-assisted warfighter enhancement research must pay particular attention to military necessity, and the medical and military cost-benefit tradeoffs of emerging technologies, all attending to the unique status of warfighters as experimental subjects. Finally, our focus is the enhancement of friendly or compatriot warfighters and not, as others have focused, enhancements intended to pacify enemy warfighters.

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.

Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems Using Feature Importance Fusion

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features’ importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance in estimates and to improve the quality of explanations. Our hypothesis is that this leads to more robust and trustworthy explanations of the contribution of each feature to machine learning predictions. To test this hypothesis, we propose an extensible model-agnostic framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models, (ii) predictive machine learning, (iii) feature importance quantification, and (iv) feature importance decision fusion using an ensemble strategy. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the quality of the feature importance ensembles studied. The results show that, overall, our feature importance ensemble framework produces 15% less feature importance errors compared with existing methods. Additionally, the results reveal that different levels of noise in the datasets do not affect the feature importance ensembles’ ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features. We also discuss the implications of our findings on the quality of explanations provided to safety-critical systems.

Fostering the trustworthiness of researchers: SPECS and the role of ethical reflexivity in novel neurotechnology research

The development of novel neurotechnologies, such as brain-computer interface (BCI) and deep-brain stimulation (DBS), are very promising in improving the welfare and life prospects many people. These include life-changing therapies for medical conditions and enhancements of cognitive, emotional, and moral capacities. Yet there are also numerous moral risks and uncertainties involved in developing novel neurotechnologies. For this reason, the progress of novel neurotechnology research requires that diverse publics place trust in researchers to develop neural interfaces in ways that are overall beneficial to society and responsive to ethical values and concerns. In this article, we argue that researchers and research institutions have a moral responsibility to foster and demonstrate trustworthiness with respect to broader publics whose lives will be affected by their research. Using Annette Baier’s conceptual analysis of trust, which takes competence and good will to be its central components, we propose that practices of ethical reflexivity could play a valuable role in fostering the trustworthiness of individual researchers and research institutions through building and exhibiting their moral competence and good will. By ethical reflexivity, we mean the reflective and discursive activity of articulating, analyzing, and assessing the assumptions and values that might be underlying their ethical actions and projects. Here, we share an ethics dialog tool—called the Scientific Perspectives and Ethics Commitments Survey (or SPECS)—developed by the University of Washington’s Center of Neurotechnology (CNT) Neuroethics Thrust. Ultimately, the aim is to show the promise of ethical reflexivity practices, like SPECS, as a method of enhancing trustworthiness in researchers and their institutions that seek to develop novel neurotechnologies for the overall benefit of society.

Neuroethics Inside and Out: A Comparative Survey of Neural Device Industry Representatives and the General Public on Ethical Issues and Principles in Neurotechnology

Neurotechnologies are rapidly being developed with the aim of alleviating suffering caused by disease and assisting individuals with various disabilities. As the capabilities and applications of neural devices advance, potential ethical challenges related to agency, identity, privacy, equality, normality and justice have been noted. We sought to explore attitudes toward these ethical challenges in two important, but understudied groups of stakeholders-members of the neural device industry and members of the general public. Survey responses from 66 industry professionals and 1088 members of the general public who do not work with neural devices were collected. After controlling for demographic differences between the groups (industry vs. general public; age, gender, racial/ethnic background), we found a large degree of consistency between the groups in their attitudes toward the ethical topic areas and the need for guiding ethical principles, but also some differences related to privacy, consent, and confidence in the neural device industry to incorporate ethical concerns into the design process. These data have implications for industry professionals tasked with designing and disseminating new neural devices, end-users of their products, and stakeholders at each step in between who must navigate the rapidly-growing landscape of advances in neurotechnology.

Citizen Neuroscience: Brain-Computer Interface Researcher Perspectives on Do-It-Yourself Brain Research

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.

Ethical Challenges of Risk, Informed Consent, and Posttrial Responsibilities in Human Research With Neural Devices: A Review

Importance

Developing more and better diagnostic and therapeutic tools for central nervous system disorders is an ethical imperative. Human research with neural devices is important to this effort and a critical focus of the National Institutes of Health Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Despite regulations and standard practices for conducting ethical research, researchers and others seek more guidance on how to ethically conduct neural device studies. This article draws on, reviews, specifies, and interprets existing ethical frameworks, literature, and subject matter expertise to address 3 specific ethical challenges in neural devices research: analysis of risk, informed consent, and posttrial responsibilities to research participants.

Observations

Research with humans proceeds after careful assessment of the risks and benefits. In assessing whether risks are justified by potential benefits in both invasive and noninvasive neural device research, the following categories of potential risks should be considered: those related to surgery, hardware, stimulation, research itself, privacy and security, and financial burdens. All 3 of the standard pillars of informed consent-disclosure, capacity, and voluntariness-raise challenges in neural device research. Among these challenges are the need to plan for appropriate disclosure of information about atypical and emerging risks of neural devices, a structured evaluation of capacity when that is in doubt, and preventing patients from feeling unduly pressured to participate. Researchers and funders should anticipate participants' posttrial needs linked to study participation and take reasonable steps to facilitate continued access to neural devices that benefit participants. Possible mechanisms for doing so are explored here. Depending on the study, researchers and funders may have further posttrial responsibilities.

Conclusions and Relevance

This ethical analysis and points to consider may assist researchers, institutional review boards, funders, and others engaged in human neural device research.

Neural engineering: the process, applications, and its role in the future of medicine

OBJECTIVE

Recent advances in neural engineering have restored mobility to people with paralysis, relieved symptoms of movement disorders, reduced chronic pain, restored the sense of hearing, and provided sensory perception to individuals with sensory deficits.

APPROACH

This progress was enabled by the team-based, interdisciplinary approaches used by neural engineers. Neural engineers have advanced clinical frontiers by leveraging tools and discoveries in quantitative and biological sciences and through collaborations between engineering, science, and medicine. The movement toward bioelectronic medicines, where neuromodulation aims to supplement or replace pharmaceuticals to treat chronic medical conditions such as high blood pressure, diabetes and psychiatric disorders is a prime example of a new frontier made possible by neural engineering. Although one of the major goals in neural engineering is to develop technology for clinical applications, this technology may also offer unique opportunities to gain insight into how biological systems operate.

MAIN RESULTS

Despite significant technological progress, a number of ethical and strategic questions remain unexplored. Addressing these questions will accelerate technology development to address unmet needs. The future of these devices extends far beyond treatment of neurological impairments, including potential human augmentation applications. Our task, as neural engineers, is to push technology forward at the intersection of disciplines, while responsibly considering the readiness to transition this technology outside of the laboratory to consumer products.

SIGNIFICANCE

This article aims to highlight the current state of the neural engineering field, its links with other engineering and science disciplines, and the challenges and opportunities ahead. The goal of this article is to foster new ideas for innovative applications in neurotechnology.