Brain leaks and consumer neurotechnology

The speech neuroprosthesis

Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore natural communication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinical epilepsy monitoring and subsequently in individuals with paralysis as part of early feasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapid and substantial progress in decoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.

Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.

Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness.

Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces

Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics.Approach. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders.Main results. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications.Significance. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.

Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to "mental privacy." In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are-at least for now-no different from those raised by other well-understood data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum's "contextual integrity" theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts-healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy.

Ethical considerations for integrating multimodal computer perception and neurotechnology

Background

Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures.

Methods

We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis.

Results

Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data.

Discussion

Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.

Advocating for neurodata privacy and neurotechnology regulation

The ability to record and alter brain activity by using implantable and nonimplantable neural devices, while poised to have significant scientific and clinical benefits, also raises complex ethical concerns. In this Perspective, we raise awareness of the ability of artificial intelligence algorithms and data-aggregation tools to decode and analyze data containing highly sensitive information, jeopardizing personal neuroprivacy. Voids in existing regulatory frameworks, in fact, allow unrestricted decoding and commerce of neurodata. We advocate for the implementation of proposed ethical and human rights guidelines, alongside technical options such as data encryption, differential privacy and federated learning to ensure the protection of neurodata privacy. We further encourage regulatory bodies to consider taking a position of responsibility by categorizing all brain-derived data as sensitive health data and apply existing medical regulations to all data gathered via pre-registered neural devices. Lastly, we propose that a technocratic oath may instill a deontology for neurotechnology practitioners akin to what the Hippocratic oath represents in medicine. A conscientious societal position that thoroughly rejects the misuse of neurodata would provide the moral compass for the future development of the neurotechnology field.

User Identity Protection in EEG-Based Brain-Computer Interfaces

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.

The Mystery of Mental Integrity: Clarifying Its Relevance to Neurotechnologies

The concept of mental integrity is currently a significant topic in discussions concerning the regulation of neurotechnologies. Technologies such as deep brain stimulation and brain-computer interfaces are believed to pose a unique threat to mental integrity, and some authors have advocated for a legal right to protect it. Despite this, there remains uncertainty about what mental integrity entails and why it is important. Various interpretations of the concept have been proposed, but the literature on the subject is inconclusive. Here we consider a number of possible interpretations and argue that the most plausible one concerns neurotechnologies that bypass one's reasoning capacities, and do so specifically in ways that reliably lead to alienation from one's mental states. This narrows the scope of what constitutes a threat to mental integrity and offers a more precise role for the concept to play in the ethical evaluation of neurotechnologies.

Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System

The present study introduces a brain-computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a "neurofeedback" group, which performed motor imagery while receiving feedback, and a "control" group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual's ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.

Minding Rights: Mapping Ethical and Legal Foundations of 'Neurorights'

The rise of neurotechnologies, especially in combination with artificial intelligence (AI)-based methods for brain data analytics, has given rise to concerns around the protection of mental privacy, mental integrity and cognitive liberty - often framed as "neurorights" in ethical, legal, and policy discussions. Several states are now looking at including neurorights into their constitutional legal frameworks, and international institutions and organizations, such as UNESCO and the Council of Europe, are taking an active interest in developing international policy and governance guidelines on this issue. However, in many discussions of neurorights the philosophical assumptions, ethical frames of reference and legal interpretation are either not made explicit or conflict with each other. The aim of this multidisciplinary work is to provide conceptual, ethical, and legal foundations that allow for facilitating a common minimalist conceptual understanding of mental privacy, mental integrity, and cognitive liberty to facilitate scholarly, legal, and policy discussions.

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.

How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation

Like many ethics debates surrounding emerging technologies, neuroethics is increasingly concerned with the private sector. Here, entrepreneurial visions and claims of how neurotechnology innovation will revolutionize society-from brain-computer-interfaces to neural enhancement and cognitive phenotyping-are confronted with public and policy concerns about the risks and ethical challenges related to such innovations. But while neuroethics frameworks have a longer track record in public sector research such as the U.S. BRAIN Initiative, much less is known about how businesses-and especially start-ups-address ethics in tech development. In this paper, we investigate how actors in the field frame and enact ethics as part of their innovative R&D processes and business models. Drawing on an empirical case study on direct-to-consumer (DTC) neurotechnology start-ups, we find that actors engage in careful boundary-work to anticipate and address public critique of their technologies, which allows them to delineate a manageable scope of their ethics integration. In particular, boundaries are drawn around four areas: the technology's actual capability, purpose, safety and evidence-base. By drawing such lines of demarcation, we suggest that start-ups make their visions of ethical neurotechnology in society more acceptable, plausible and desirable, favoring their innovations while at the same time assigning discrete responsibilities for ethics. These visions establish a link from the present into the future, mobilizing the latter as promissory place where a technology's benefits will materialize and to which certain ethical issues can be deferred. In turn, the present is constructed as a moment in which ethical engagement could be delegated to permissive regulatory standards and scientific authority. Our empirical tracing of the construction of 'ethical realities' in and by start-ups offers new inroads for ethics research and governance in tech industries beyond neurotechnology.

Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity

Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here, we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance imaging data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.

The present and future of neural interfaces

The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.

Survey of Investigators About Sharing Human Research Data in the Neurosciences

BACKGROUND AND OBJECTIVES

In the neurosciences, significant opportunities for sharing individual-level data are underexploited. Commentators suggest various barriers to data sharing, which may need to be addressed. Investigators' perspectives on the main barriers are unclear. Furthermore, bioethicists have raised concerns about the potential misuse of neuroscience data, although discussions are hampered by uncertainty about the potential risks. It is unclear how common sensitive data are obtained and whether investigators judge them as sensitive.

METHODS

An online survey was disseminated among 1,190 principal investigators (PIs) of active National Institute of Neurological Disorders and Stroke, National Institute of Mental Health, or NIH Brain Research Through Advancing Innovative Neurotechnologies Initiative grants involving human subject research.

RESULTS

A total of 397 investigators responded to the survey (response rate 33%). Most investigators (84%) support efforts to increase sharing of deidentified individual-level data. However, investigators perceive many barriers to data sharing. The largest barriers were costs and time; limited interpretation of the data without understanding the context of data collection; lack of incentives; limited standardization and norms for data acquisition, formatting, and description; and heterogeneity of data types. Several types of data described as sensitive in the literature are common among neuroscience studies, for example, neural correlates of behavior, emotions, or decision making (71%) and/or predictive data (54%). Although most investigators consider it unlikely or extremely unlikely for their research data to be misused to harm individual research participants (82%), the majority were at least slightly concerned about potential harm to individuals if their research data were misused (65%). Investigators with more easily reidentifiable data, data from vulnerable groups, and neural data were more concerned about the likelihood of misuse and/or magnitude of harm of misuse of their research data.

DISCUSSION

We hope these data help prioritize the development of tools and strategies to overcome the main barriers to data sharing. Furthermore, these data provide input on what may be sensitive data for which additional safeguards should be considered.

Good scientific practice in EEG and MEG research: Progress and perspectives

Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.

Towards a Governance Framework for Brain Data

The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical implications of brain data acquisition and processing, and outline a multi-level governance framework. This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use. The framework consists of four primary areas of regulatory intervention: binding regulation, ethics and soft law, responsible innovation, and human rights.

How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

Objective. Processing strategies are analysed with respect to the classification of electroencephalographic signals related to brain-computer interfaces based on motor imagery. A review of literature is carried out to understand the achievements in motor imagery classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility.Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery- based brain-computer interfaces. Article search was carried out in accordance with the PRISMA standard and 89 studies were included.Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85 % to 100 % range for the binary case and in the 83 % to 93 % range for multi-class one. Associated uncertainties are up to 6 % while repeatability for a predetermined dataset is up to 8 %. Reproducibility assessment was instead prevented by lack of standardization in experiments.Significance. By relying on the analysed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a brain-computer interface. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of results reproducibility.

Researchers’ Ethical Concerns About Using Adaptive Deep Brain Stimulation for Enhancement

The capacity of next-generation closed-loop or adaptive deep brain stimulation devices (aDBS) to read (measure neural activity) and write (stimulate brain regions or circuits) shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively (non-therapeutically) modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies (e.g. transcranial stimulation) currently used for enhancement is that aDBS remains an invasive, surgically-implanted technology with a risk-benefit ratio significantly different when applied to diseased versus non-diseased individuals. Despite a large discourse about the ethics of enhancement, no empirical studies yet examine perspectives on enhancement from within the aDBS research community. We interviewed 23 aDBS researchers about their attitudes toward expanding aDBS use for enhancement. A thematic content analysis revealed that researchers share ethical concerns related to (1) safety and security; (2) enhancement as unnecessary, unnatural or aberrant; and (3) fairness, equality, and distributive justice. Most (70%) researchers felt that enhancement applications for DBS will eventually be technically feasible and that attempts to develop such applications for DBS are already happening (particularly for military purposes). However, researchers unanimously (100%) felt that DBS ideally should not be considered for enhancement until researchers better understand brain target localization and functioning. While many researchers acknowledged controversies highlighted by scholars and ethicists, such as potential impacts on personhood, authenticity, autonomy and privacy, their ethical concerns reflect considerations of both gravity and perceived near-term likelihood.

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.

Projections and the Potential Societal Impact of the Future of Neurotechnologies

Traditionally, recording from and stimulating the brain with high spatial and temporal resolution required invasive means. However, recently, the technical capabilities of less invasive and non-invasive neuro-interfacing technology have been dramatically improving, and laboratories and funders aim to further improve these capabilities. These technologies can facilitate functions such as multi-person communication, mood regulation and memory recall. We consider a potential future where the less invasive technology is in high demand. Will this demand match that the current-day demand for a smartphone? Here, we draw upon existing research to project which particular neuroethics issues may arise in this potential future and what preparatory steps may be taken to address these issues.

Digital Mental Health for Young People: A Scoping Review of Ethical Promises and Challenges

Mental health disorders are complex disorders of the nervous system characterized by a behavioral or mental pattern that causes significant distress or impairment of personal functioning. Mental illness is of particular concern for younger people. The WHO estimates that around 20% of the world's children and adolescents have a mental health condition, a rate that is almost double compared to the general population. One approach toward mitigating the medical and socio-economic effects of mental health disorders is leveraging the power of digital health technology to deploy assistive, preventative, and therapeutic solutions for people in need. We define “digital mental health” as any application of digital health technology for mental health assessment, support, prevention, and treatment. However, there is only limited evidence that digital mental health tools can be successfully implemented in clinical settings. Authors have pointed to a lack of technical and medical standards for digital mental health apps, personalized neurotechnology, and assistive cognitive technology as a possible cause of suboptimal adoption and implementation in the clinical setting. Further, ethical concerns have been raised related to insufficient effectiveness, lack of adequate clinical validation, and user-centered design as well as data privacy vulnerabilities of current digital mental health products. The aim of this paper is to report on a scoping review we conducted to capture and synthesize the growing literature on the promises and ethical challenges of digital mental health for young people aged 0–25. This review seeks to survey the scope and focus of the relevant literature, identify major benefits and opportunities of ethical significance (e.g., reducing suffering and improving well-being), and provide a comprehensive mapping of the emerging ethical challenges. Our findings provide a comprehensive synthesis of the current literature and offer a detailed informative basis for any stakeholder involved in the development, deployment, and management of ethically-aligned digital mental health solutions for young people.

Simulation-Based Clarification of Appropriate Factors for Presenting Phosphene in Two Directions Avoiding Electrical Interference

Walking support systems are essential for blind people. In this study, the presentation of phosphene position is focused on as a method to detect obstacles for blind people. When the phosphene is used in a walking support system, it is necessary to accurately present the phosphene in at least three directions of the visual field. Controlling the presentation of phosphene position has been reported in several previous studies. However, methodologies to present phosphene in multiple directions without any electric interference have not as yet been investigated. In this study, therefore, appropriate stimulation factors are clarified by the simulation of electric field on the eyeball surface which is strongly related to the presentation of phosphene position in the visual field. As a result of the simulation, it was revealed that the distance of each electrode does not give a significant effect to the eyeball surface. However, the phase of alternating current significantly changed the electric field on the eyeball surface. From investigation of the simulation results, it was clarified that the transition of the electric field on the eyeball surface can be controlled using anti-phase stimulation. In addition, the methodology to present the phosphene at least in two directions was verified.

Towards Neuroscience of the Everyday World (NEW) using functional Near-Infrared Spectroscopy

Functional Near-Infrared Spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional Magnetic Resonance Imaging (fMRI), expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and Electroencephalography (EEG) for routine, increasingly unconstrained, and mobile brain imaging is leading towards a new domain that we term "Neuroscience of the Everyday World" (NEW). In this light, we review recent advances in hardware, study design and signal processing, and discuss challenges and future directions towards achieving NEW.

Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology

Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user-friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical use. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement.

Digital Predictors of Morbidity, Hospitalization, and Mortality Among Older Adults: A Systematic Review and Meta-Analysis

The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has rapidly enabled new opportunities for predictive health monitoring. Leveraging digital health tools to track parameters relevant to human health is particularly important for the older segments of the population as old age is associated with multimorbidity and higher care needs. In order to assess the potential of these digital health technologies to improve health outcomes, it is paramount to investigate which digitally measurable parameters can effectively improve health outcomes among the elderly population. Currently, there is a lack of systematic evidence on this topic due to the inherent heterogeneity of the digital health domain and the lack of clinical validation of both novel prototypes and marketed devices. For this reason, the aim of the current study is to synthesize and systematically analyse which digitally measurable data may be effectively collected through digital health devices to improve health outcomes for older people. Using a modified PICO process and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we provide the results of a systematic review and subsequent meta-analysis of digitally measurable predictors of morbidity, hospitalization, and mortality among older adults aged 65 or older. These findings can inform both technology developers and clinicians involved in the design, development and clinical implementation of digital health technologies for elderly citizens.

A review of user training methods in brain computer interfaces based on mental tasks

Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training - notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.

Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges

Clinical neuroscience is increasingly relying on the collection of large volumes of differently structured data and the use of intelligent algorithms for data analytics. In parallel, the ubiquitous collection of unconventional data sources (e.g. mobile health, digital phenotyping, consumer neurotechnology) is increasing the variety of data points. Big data analytics and approaches to Artificial Intelligence (AI) such as advanced machine learning are showing great potential to make sense of these larger and heterogeneous data flows. AI provides great opportunities for making new discoveries about the brain, improving current preventative and diagnostic models in both neurology and psychiatry and developing more effective assistive neurotechnologies. Concurrently, it raises many new methodological and ethical challenges. Given their transformative nature, it is still largely unclear how AI-driven approaches to the study of the human brain will meet adequate standards of scientific validity and affect normative instruments in neuroethics and research ethics. This manuscript provides an overview of current AI-driven approaches to clinical neuroscience and an assessment of the associated key methodological and ethical challenges. In particular, it will discuss what ethical principles are primarily affected by AI approaches to human neuroscience, and what normative safeguards should be enforced in this domain.

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.

Narrative Devices: Neurotechnologies, Information, and Self-Constitution

This article provides a conceptual and normative framework through which we may understand the potentially ethically significant roles that information generated by neurotechnologies about our brains and minds may play in our construction of our identities. Neuroethics debates currently focus disproportionately on the ways that third parties may (ab)use these kinds of information. These debates occlude interests we may have in whether and how we ourselves encounter information about our own brains and minds. This gap is not yet adequately addressed by most allusions in the literature to potential identity impacts. These lack the requisite conceptual or normative foundations to explain why we should be concerned about such effects or how they might be addressed. This article seeks to fill this gap by presenting a normative account of identity as constituted by embodied self-narratives. It proposes that information generated by neurotechnologies can play significant content-supplying and interpretive roles in our construction of our self-narratives. It argues, to the extent that these roles support and detract from the coherence and inhabitability of these narratives, access to information about our brains and minds engages non-trivial identity-related interests. These claims are illustrated using examples drawn from empirical literature reporting reactions to information generated by implantable predictive BCIs and psychiatric neuroimaging. The article concludes by highlighting ways in which information generated by neurotechnologies might be governed so as to protect information subjects' interests in developing and inhabiting their own identities.

The Emerging Neurobioeconomy: Implications for National Security

Neuroscience and neurotechnology (neuroS/T) are techniques and tools used to assess or affect the nervous system. Current and near-future developments are enabling an expanding palette of capabilities to understand and influence brain functions that can foster wellbeing and economic growth. This "neurobioeconomy" is rapidly growing, attributable in large part to the global dissemination of knowledge that fosters and contributes to scientific innovation, invention, and commercialization. As a result, several countries have initiated programs in brain research and innovation. Not all brain sciences engender security concerns, but a predominance in global biomedical, bioengineering, wellness/lifestyle, and defense markets enables considerable power. Such power can be leveraged in nonkinetic or kinetic domains, and several countries have identified neuroS/T as viable and of growing value for use in warfare, intelligence, and national security operations. In addition to the current focus on biotechnology, the United States and its allies must acknowledge the significance of brain science and its projected impact on the economy, national security, and lifestyles. In this article, we examine growth of the neuroS/T market, discuss how the neurobioeconomy poses distinct ethical and security issues for the broader bioeconomy, provide examples of such issues that arise from specific nation-state activity and technological commercialization, and propose a risk assessment and mitigation approach that can be engaged by the economic, scientific, and security communities.

Brain Recording, Mind-Reading, and Neurotechnology: Ethical Issues from Consumer Devices to Brain-Based Speech Decoding

Brain reading technologies are rapidly being developed in a number of neuroscience fields. These technologies can record, process, and decode neural signals. This has been described as 'mind reading technology' in some instances, especially in popular media. Should the public at large, be concerned about this kind of technology? Can it really read minds? Concerns about mind-reading might include the thought that, in having one's mind open to view, the possibility for free deliberation, and for self-conception, are eroded where one isn't at liberty to privately mull things over. Themes including privacy, cognitive liberty, and self-conception and expression appear to be areas of vital ethical concern. Overall, this article explores whether brain reading technologies are really mind reading technologies. If they are, ethical ways to deal with them must be developed. If they are not, researchers and technology developers need to find ways to describe them more accurately, in order to dispel unwarranted concerns and address appropriately those that are warranted.

Privacy Challenges to the Democratization of Brain Data

Wearable devices that record brain signals may present privacy concerns for consumers. Industry leaders discussed four such concerns with us that pertain to data collection and management, user autonomy and information transparency, exceptionalism of brain data, and regulatory systems.

Neuroenhancement for sale: assessing the website claims of neurofeedback providers in the United States

Although electroencephalographic (EEG) neurofeedback is a technique that has been in existence for many decades, it has remained controversial, largely due to questions about efficacy. Yet neurofeedback is being widely offered to the public, often at great expense. To date, however, there has not been empirical data on which providers are utilizing neurofeedback, what they are offering it for, and how they are advertising the technique. The present study aimed to fill that gap by systematically analyzing the websites of neurofeedback practitioners in the United States. To that end, we obtained data from four directories of neurofeedback providers, extracting practitioner names, geographical locations, professional training, and website URLs. Only websites offering neurofeedback services (N=371) were included in the next step, wherein two coders independently coded the websites based on a codebook developed from preliminary analyses. We found that nearly all websites (97.0%) contained claims about at least one clinical indication, most commonly anxiety, ADHD/ADD, and depression; however, only 36.0% of providers had either a medical degree (MD) or a doctoral-level degree in psychology. The majority of websites advertised neurofeedback for cognitive (90.0%) or performance (67.9%) enhancement, and roughly three-quarters utilized language related to complementary and alternative medicine (CAM). In sum, there is a considerable divergence between the scientific literature on neurofeedback and the marketing of neurofeedback services to the general public, raising concerns regarding the misrepresentation of services and misleading advertising claims.

"Hunting Down My Son's Killer": New Roles of Patients in Treatment Discovery and Ethical Uncertainty

The past few years have witnessed several media-covered cases involving citizens actively engaging in the pursuit of experimental treatments for their medical conditions-or those of their loved ones-in the absence of established standards of therapy. This phenomenon is particularly observable in patients with rare genetic diseases, as the development of effective therapies for these disorders is hindered by the limited profitability and market value of pharmaceutical research. Sociotechnical trends at the cross-section of medicine and society are facilitating the involvement of patients and creating the digital infrastructure necessary to its sustainment. Such participant-led research (PLR) has the potential to promote the autonomy of research participants as drivers of discovery and to open novel non-canonical avenues of scientific research. At the same time, however, the extra-institutional, self-appointed, and, often, oversight-free nature of PLR raises ethical concern. This paper explores the complex ethical entanglement of PLR by critically appraising case studies and discussing the conditions for its moral justification. Furthermore, we propose a path forward to ensure the safe and effective implementation of PLR within the current research ecosystem in a manner that maximizes the benefits for both individual participants and society at large, while minimizing the risks.

How to Accurately and Privately Identify Anomalies

Identifying anomalies in data is central to the advancement of science, national security, and finance. However, privacy concerns restrict our ability to analyze data. Can we lift these restrictions and accurately identify anomalies without hurting the privacy of those who contribute their data? We address this question for the most practically relevant case, where a record is considered anomalous relative to other records. We make four contributions. First, we introduce the notion of sensitive privacy, which conceptualizes what it means to privately identify anomalies. Sensitive privacy generalizes the important concept of differential privacy and is amenable to analysis. Importantly, sensitive privacy admits algorithmic constructions that provide strong and practically meaningful privacy and utility guarantees. Second, we show that differential privacy is inherently incapable of accurately and privately identifying anomalies; in this sense, our generalization is necessary. Third, we provide a general compiler that takes as input a differentially private mechanism (which has bad utility for anomaly identification) and transforms it into a sensitively private one. This compiler, which is mostly of theoretical importance, is shown to output a mechanism whose utility greatly improves over the utility of the input mechanism. As our fourth contribution we propose mechanisms for a popular definition of anomaly ((β, r)-anomaly) that (i) are guaranteed to be sensitively private, (ii) come with provable utility guarantees, and (iii) are empirically shown to have an overwhelmingly accurate performance over a range of datasets and evaluation criteria.

Neuroethics at 15: The Current and Future Environment for Neuroethics

Neuroethics research and scholarship intersect with dynamic academic disciplines in science, engineering, and the humanities. On the occasion of the 15th anniversary of the formation of the International Neuroethics Society, we identify current and future topics for neuroethics and discuss the many social and political challenges that emerge from the converging dynamics of neurotechnologies and artificial intelligence. We also highlight the need for a global, transdisciplinary, and integrated community of researchers to address the challenges that are precipitated by this rapid sociotechnological transformation.