Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

Deciphering platinum dissolution in neural stimulation electrodes: Electrochemistry or biology?

Platinum (Pt) is the metal of choice for electrodes in implantable neural prostheses like the cochlear implants, deep brain stimulating devices, and brain-computer interfacing technologies. However, it is well known since the 1970s that Pt dissolution occurs with electrical stimulation. More recent clinical and in vivo studies have shown signs of corrosion in explanted electrode arrays and the presence of Pt-containing particulates in tissue samples. The process of degradation and release of metallic ions and particles can significantly impact on device performance. Moreover, the effects of Pt dissolution products on tissue health and function are still largely unknown. This is due to the highly complex chemistry underlying the dissolution process and the difficulty in decoupling electrical and chemical effects on biological responses. Understanding the mechanisms and effects of Pt dissolution proves challenging as the dissolution process can be influenced by electrical, chemical, physical, and biological factors, all of them highly variable between experimental settings. By evaluating comprehensive findings on Pt dissolution mechanisms reported in the fuel cell field, this review presents a critical analysis of the possible mechanisms that drive Pt dissolution in neural stimulation in vitro and in vivo. Stimulation parameters, such as aggregate charge, charge density, and electrochemical potential can all impact the levels of dissolved Pt. However, chemical factors such as electrolyte types, dissolved gases, and pH can all influence dissolution, confounding the findings of in vitro studies with multiple variables. Biological factors, such as proteins, have been documented to exhibit a mitigating effect on the dissolution process. Other biological factors like cells and fibro-proliferative responses, such as fibrosis and gliosis, impact on electrode properties and are suspected to impact on Pt dissolution. However, the relationship between electrical properties of stimulating electrodes and Pt dissolution remains contentious. Host responses to Pt degradation products are also controversial due to the unknown chemistry of Pt compounds formed and the lack of understanding of Pt distribution in clinical scenarios. The cytotoxicity of Pt produced via electrical stimulation appears similar to Pt-based compounds, including hexachloroplatinates and chemotherapeutic agents like cisplatin. While the levels of Pt produced under clinical and acute stimulation regimes were typically an order of magnitude lower than toxic concentrations observed in vitro, further research is needed to accurately assess the mass balance and type of Pt produced during long-term stimulation and its impact on tissue response. Finally, approaches to mitigating the dissolution process are reviewed. A wide variety of approaches, including stimulation strategies, coating electrode materials, and surface modification techniques to avoid excess charge during stimulation and minimise tissue response, may ultimately support long-term and safe operation of neural stimulating devices.

Right to mental integrity and neurotechnologies: implications of the extended mind thesis

The possibility of neurotechnological interference with our brain and mind raises questions about the moral rights that would protect against the (mis)use of these technologies. One such moral right that has received recent attention is the right to mental integrity. Though the metaphysical boundaries of the mind are a matter of live debate, most defences of this moral right seem to assume an internalist (brain-based) view of the mind. In this article, we will examine what an extended account of the mind might imply for the right to mental integrity and the protection it provides against neurotechnologies. We argue that, on an extended account of the mind, the scope of the right to mental integrity would expand significantly, implying that neurotechnologies would no longer pose a uniquely serious threat to the right. In addition, some neurotechnologies may even be protected by the right to mental integrity, as the technologies would become part of the mind. We conclude that adopting an extended account of the mind has significant implications for the right to mental integrity in terms of its protective scope and capacity to protect against neurotechnologies, demonstrating that metaphysical assumptions about the mind play an important role in determining the moral protection provided by the right.

Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment

Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.

Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making

Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

Neurotechnology and ethics guidelines for human enhancement: The case of the hippocampal cognitive prosthesis

Neurotechnologies offer both therapeutic and enhancement potential. In this article, we demonstrate how ethics guidelines can help with critical reflection on their potential for enhancement. We do this through the case of the hippocampal cognitive prosthesis. This prothesis developed in the US, has primarily therapeutic ends, with scope for enhancement. This technology raises several ethical issues, including as related to identity and memory, autonomy and authenticity. In the first section, we outline what we mean by enhancement, and introduce neurotechnologies generally and the hippocampal cognitive prosthesis specifically, with an introduction to generally relevant ethical issues. In the second section, we outline ethical issues pertinent to the hippocampal cognitive prosthesis and explore how ethics guidelines can help to promote essential critical reflection on a technology like this. Through all this, our emphasis is to balance between technological optimism and caution, especially where technologies have enhancement potential.

Development of Korea Neuroethics Guidelines

BACKGROUND

Advances in neuroscience and neurotechnology provide great benefits to humans though unknown challenges may arise. We should address these challenges using new standards as well as existing ones. Novel standards should include ethical, legal, and social aspects which would be appropriate for advancing neuroscience and technology. Therefore, the Korea Neuroethics Guidelines were developed by stakeholders related to neuroscience and neurotechnology, including experts, policy makers, and the public in the Republic of Korea.

METHOD

The guidelines were drafted by neuroethics experts, were disclosed at a public hearing, and were subsequently revised by opinions of various stakeholders.

RESULTS

The guidelines are composed of twelve issues; humanity or human dignity, individual personality and identity, social justice, safety, sociocultural prejudice and public communication, misuse of technology, responsibility for the use of neuroscience and technology, specificity according to the purpose of using neurotechnology, autonomy, privacy and personal information, research, and enhancement.

CONCLUSION

Although the guidelines may require a more detailed discussion after future advances in neuroscience and technology or changes in socio-cultural milieu, the development of the Korea Neuroethics Guidelines is a milestone for the scientific community and society in general for the ongoing development in neuroscience and neurotechnology.

Identifying risk controls for future advanced brain-computer interfaces: A prospective risk assessment approach using work domain analysis

Brain-computer interface (BCI) technologies are progressing rapidly and may eventually be implemented widely within society, yet their risks have arguably not yet been comprehensively identified, nor understood. This study analysed an anticipated invasive BCI system lifecycle to identify the individual, organisational, and societal risks associated with BCIs, and controls that could be used to mitigate or eliminate these risks. A BCI system lifecycle work domain analysis model was developed and validated with 10 subject matter experts. The model was subsequently used to undertake a systems thinking-based risk assessment approach to identify risks that could emerge when functions are either undertaken sub-optimally or not undertaken at all. Eighteen broad risk themes were identified that could negatively impact the BCI system lifecycle in a variety of unique ways, while a larger number of controls for these risks were also identified. The most concerning risks included inadequate regulation of BCI technologies and inadequate training of BCI stakeholders, such as users and clinicians. In addition to specifying a practical set of risk controls to inform BCI device design, manufacture, adoption, and utilisation, the results demonstrate the complexity involved in managing BCI risks and suggests that a system-wide coordinated response is required. Future research is required to evaluate the comprehensiveness of the identified risks and the practicality of implementing the risk controls.

Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design

OBJECTIVE

We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed.

BACKGROUND

Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics.

RESULTS

Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches-like blink-evoked potentials-render event-related analyses of the EEG possible also during unrestricted behavior.

CONCLUSION

Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don't have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.

Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses

Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can predict the neural or perceptual response to an electrical stimulus, an optimal stimulation strategy solves the inverse problem: what is the required stimulus to produce a desired response? Here, we frame this as an end-to-end optimization problem, where a deep neural network stimulus encoder is trained to invert a known and fixed forward model that approximates the underlying biological system. As a proof of concept, we demonstrate the effectiveness of this hybrid neural autoencoder (HNA) in visual neuroprostheses. We find that HNA produces high-fidelity patient-specific stimuli representing handwritten digits and segmented images of everyday objects, and significantly outperforms conventional encoding strategies across all simulated patients. Overall this is an important step towards the long-standing challenge of restoring high-quality vision to people living with incurable blindness and may prove a promising solution for a variety of neuroprosthetic technologies.

The Ethics of Supernumerary Robotic Limbs. An Enactivist Approach

Supernumerary robotic limbs are innovative devices in the field of wearable robotics which can provide humans with unprecedented sensorimotor abilities. However, scholars have raised awareness of the ethical issues that would arise from the large adoption of technologies for human augmentation in society. Most negative attitudes towards such technologies seem to rely on an allegedly clear distinction between therapy and enhancement in the use of technological devices. Based on such distinction, people tend to accept technologies when used for therapeutic purposes (e.g., prostheses), but tend to raise issues when similar devices are used for upgrading a physical or cognitive ability (e.g., supernumerary robotics limbs). However, as many scholars have pointed out, the distinction between therapy and enhancement might be theoretically flawed. In this paper, we present an alternative approach to the ethics of supernumerary limbs which is based on two related claims. First, we propose to conceive supernumerary limbs as tools that necessarily modify our psychological and bodily identity. At the same time, we stress that such a modification is not ethically bad in itself; on the contrary, it drives human interaction with the environment. Second, by comparing our view with the extended mind thesis, we claim that the mediation through tools is crucial for the formation of novel meanings and skills that constitute human interaction with the world. We will relate the latter claim to enactivism as a helpful theoretical perspective to frame issues related to artificial limbs and, more in general, to technologies for augmentation. Based on this approach, we finally sketch some suggestions for future directions in the ethics of supernumerary limbs.

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.

Brain augmentation and neuroscience technologies: current applications, challenges, ethics and future prospects

Ever since the dawn of antiquity, people have strived to improve their cognitive abilities. From the advent of the wheel to the development of artificial intelligence, technology has had a profound leverage on civilization. Cognitive enhancement or augmentation of brain functions has become a trending topic both in academic and public debates in improving physical and mental abilities. The last years have seen a plethora of suggestions for boosting cognitive functions and biochemical, physical, and behavioral strategies are being explored in the field of cognitive enhancement. Despite expansion of behavioral and biochemical approaches, various physical strategies are known to boost mental abilities in diseased and healthy individuals. Clinical applications of neuroscience technologies offer alternatives to pharmaceutical approaches and devices for diseases that have been fatal, so far. Importantly, the distinctive aspect of these technologies, which shapes their existing and anticipated participation in brain augmentations, is used to compare and contrast them. As a preview of the next two decades of progress in brain augmentation, this article presents a plausible estimation of the many neuroscience technologies, their virtues, demerits, and applications. The review also focuses on the ethical implications and challenges linked to modern neuroscientific technology. There are times when it looks as if ethics discussions are more concerned with the hypothetical than with the factual. We conclude by providing recommendations for potential future studies and development areas, taking into account future advancements in neuroscience innovation for brain enhancement, analyzing historical patterns, considering neuroethics and looking at other related forecasts.

Enhancing performance with multisensory cues in a realistic target discrimination task

Making decisions is an important aspect of people’s lives. Decisions can be highly critical in nature, with mistakes possibly resulting in extremely adverse consequences. Yet, such decisions have often to be made within a very short period of time and with limited information. This can result in decreased accuracy and efficiency. In this paper, we explore the possibility of increasing speed and accuracy of users engaged in the discrimination of realistic targets presented for a very short time, in the presence of unimodal or bimodal cues. More specifically, we present results from an experiment where users were asked to discriminate between targets rapidly appearing in an indoor environment. Unimodal (auditory) or bimodal (audio-visual) cues could shortly precede the target stimulus, warning the users about its location. Our findings show that, when used to facilitate perceptual decision under time pressure, and in condition of limited information in real-world scenarios, spoken cues can be effective in boosting performance (accuracy, reaction times or both), and even more so when presented in bimodal form. However, we also found that cue timing plays a critical role and, if the cue-stimulus interval is too short, cues may offer no advantage. In a post-hoc analysis of our data, we also show that congruency between the response location and both the target location and the cues, can interfere with the speed and accuracy in the task. These effects should be taken in consideration, particularly when investigating performance in realistic tasks.

A meta-learning BCI for estimating decision confidence

OBJECTIVE

We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of Brain-Computer Interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods.

APPROACH

We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from EEG and EOG data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants' data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called Domain Adversarial Neural Networks (DANN), a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm.

MAIN RESULTS

The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period.

SIGNIFICANCE

Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.

Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury

DBS Think Tank IX was held on August 25-27, 2021 in Orlando FL with US based participants largely in person and overseas participants joining by video conferencing technology. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging deep brain stimulation (DBS) technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank IX speakers was that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. As such, this year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia and Australia; cutting-edge technologies, neuroethics, interventional psychiatry, adaptive DBS, neuromodulation for pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury.

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.

A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.

Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs

Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.

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.

An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals

This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain-computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it.

Anytime collaborative brain-computer interfaces for enhancing perceptual group decision-making

In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

Interface, interaction, and intelligence in generalized brain-computer interfaces

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.

Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market

Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1-4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments.

Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making

Objective.In many real-world decision tasks, the information available to the decision maker is incomplete. To account for this uncertainty, we associate a degree of confidence to every decision, representing the likelihood of that decision being correct. In this study, we analyse electroencephalography (EEG) data from 68 participants undertaking eight different perceptual decision-making experiments. Our goals are to investigate (1) whether subject- and task-independent neural correlates of decision confidence exist, and (2) to what degree it is possible to build brain computer interfaces that can estimate confidence on a trial-by-trial basis. The experiments cover a wide range of perceptual tasks, which allowed to separate the task-related, decision-making features from the task-independent ones.Approach.Our systems train artificial neural networks to predict the confidence in each decision from EEG data and response times. We compare the decoding performance with three training approaches: (1) single subject, where both training and testing data were acquired from the same person; (2) multi-subject, where all the data pertained to the same task, but the training and testing data came from different users; and (3) multi-task, where the training and testing data came from different tasks and subjects. Finally, we validated our multi-task approach using data from two additional experiments, in which confidence was not reported.Main results.We found significant differences in the EEG data for different confidence levels in both stimulus-locked and response-locked epochs. All our approaches were able to predict the confidence between 15% and 35% better than the corresponding reference baselines.Significance.Our results suggest that confidence in perceptual decision making tasks could be reconstructed from neural signals even when using transfer learning approaches. These confidence estimates are based on the decision-making process rather than just the confidence-reporting process.

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.

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials

The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primates. Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic. Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band (30-250 Hz), peaking between 60 to 120 Hz. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain-machine interfaces for the development of new therapeutic strategies.

MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer

Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated from spontaneous ones using short segments of magnetoencephalography (MEG) data measured immediately after the fixation onset. Recently proposed convolutional neural networks (CNNs), linear finite impulse response filters CNN (LF-CNN) and vector autoregressive CNN (VAR-CNN), were applied for binary classification of the MEG signals related to spontaneous and voluntary eye fixations collected in healthy participants (n = 25) who performed a game-like task by fixating on targets voluntarily for 500 ms or longer. Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0.66 ± 0.07 for LF-CNN and 0.67 ± 0.07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0.91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis. Current results for intention detection prior to gaze-based interface's feedback, however, are not sufficient for online single-trial eye fixation classification using MEG data alone, and further work is needed to find out if it could be used in practical applications.

The potential of noisy galvanic vestibular stimulation for optimizing and assisting human performance

Noisy galvanic vestibular stimulation (nGVS) is an emerging non-invasive brain stimulation technique. It involves applying alternating currents of different frequencies and amplitudes presented in a random, or noisy, manner through electrodes on the mastoid bones behind the ears. Because it directly activates vestibular hair cells and afferents and has an indirect effect on a variety of brain regions, it has the potential to impact many different functions. The objective of this review is twofold: (1) to review how nGVS affects motor, sensory, and cognitive performance in healthy adults; and (2) to discuss potential clinical applications of nGVS. First, we introduce the technique. We then describe the regions receiving and processing vestibular information. Next, we discuss the effects of nGVS on motor, sensory, and cognitive function in healthy adults. Subsequently, we outline its potential clinical applications. Finally, we highlight other electrical stimulation technologies and discuss why nGVS offers an alternative or complementary approach. Overall, nGVS appears promising for optimizing human performance and as an assistive technology, though further research is required.

A Functional Model for Unifying Brain Computer Interface Terminology

Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Terminology is developed from the basis of a BCI functional model. This paper provides the current status of the P2731 working group's progress towards developing a BCI terminology standard and functional model for the IEEE.

Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks

Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.

Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology

Rapid developments are currently taking place in the fields of artificial intelligence (AI) and biotechnology, and applications arising from the convergence of these 2 fields are likely to offer immense opportunities that could greatly benefit human health and biosecurity. The combination of AI and biotechnology could potentially lead to breakthroughs in precision medicine, improved biosurveillance, and discovery of novel medical countermeasures as well as facilitate a more effective public health emergency response. However, as is the case with many preceding transformative technologies, new opportunities often present new risks in parallel. Understanding the current and emerging risks at the intersection of AI and biotechnology is crucial for health security specialists and unlikely to be achieved by examining either field in isolation. Uncertainties multiply as technologies merge, showcasing the need to identify robust assessment frameworks that could adequately analyze the risk landscape emerging at the convergence of these 2 domains.This paper explores the criteria needed to assess risks associated with Artificial intelligence and biotechnology and evaluates 3 previously published risk assessment frameworks. After highlighting their strengths and limitations and applying to relevant Artificial intelligence and biotechnology examples, the authors suggest a hybrid framework with recommendations for future approaches to risk assessment for convergent technologies.

How Can Physiological Computing Benefit Human-Robot Interaction?

As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations. The growing number of neurophysiology and machine learning tools now allows for efficient operators’ MS monitoring. Sending feedback on MS in a closed-loop solution is therefore at hand. Involving a consistent automated planning technique to handle such a process could be a significant asset. This perspective article was meant to provide the reader with a synthesis of the significant literature with a view to implementing systems that adapt to the operator’s MS to improve human-robot operations’ safety and performance. First of all, the need for this approach is detailed regarding remote operation, an example of HRI. Then, several MS identified as crucial for this type of HRI are defined, along with relevant electrophysiological markers. A focus is made on prime degraded MS linked to time-on-task and task demands, as well as collateral MS linked to system outputs (i.e., feedback and alarms). Lastly, the principle of symbiotic HRI is detailed and one solution is proposed to include the operator state vector into the system using a mixed-initiative decisional framework to drive such an interaction.

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.

Tracing Pilots' Situation Assessment by Neuroadaptive Cognitive Modeling

This study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots' perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots' perception and responses to alerts, cognitive assistance can be provided based on individual needs to ensure they maintain adequate situation awareness. Data from 24 participating aircrew in a simulated flight study that included multiple alerts and air traffic control messages in single pilot setup are presented. A classifier was trained to identify pilots' neurophysiological reactions to alerts and messages from participants' electroencephalogram (EEG). A neuroadaptive ACT-R model using EEG data was compared to a conventional normative model regarding accuracy in representing individual pilots. Results show that passive BCI can distinguish between alerts that are processed by the pilot as task-relevant or irrelevant in the cockpit based on the recorded EEG. The neuroadaptive model's integration of this data resulted in significantly higher performance of 87% overall accuracy in representing individual pilots' responses to alerts and messages compared to 72% accuracy of a normative model that did not consider EEG data. We conclude that neuroadaptive technology allows for implicit measurement and tracing of pilots' perception and processing of alerts on the flight deck. Careful handling of uncertainties inherent to passive BCI and cognitive modeling shows how the representation of pilot cognitive states can be improved iteratively for providing assistance.

Neuromodulation of Gamma-Range Auditory Steady-State Responses: A Scoping Review of Brain Stimulation Studies

Neural oscillations represent a fundamental mechanism that enables coordinated action during normal brain functioning. Auditory steady-state responses (ASSRs) are used to test the ability to generate gamma-range activity. Different non-invasive brain stimulation (NIBS) techniques have the potential to modulate neural activation patterns that are aberrant in a variety of neuropsychiatric disorders. Here, we summarize the current state of knowledge on how different methods of NIBS (transcranial altering current stimulation—tACS, transcranial direct current stimulation—tDCS, transcranial random noise stimulation—tRNS, paired associative stimulation—PAS, repetitive transcranial magnetic stimulation—rTMS) affect the gamma-range ASSRs in both healthy and clinical populations. We show that the current research has been far from systematic and methodologically heterogeneous. Nevertheless, some brain stimulation techniques, especially tACS and rTMS show strong potential for further exploration. We outline the main findings and provide directions for further research into neuromodulation of ASSRs as a promising biomarker of different psychopathological conditions such as schizophrenia, bipolar disorder, attention deficit hyperactivity disorder (ADHD), autism.

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

The assessment and prediction of cognitive performance is a key issue for any discipline concerned with human operators in the context of safety-critical behavior. Most of the research has focused on the measurement of mental workload but this construct remains difficult to operationalize despite decades of research on the topic. Recent advances in Neuroergonomics have expanded our understanding of neurocognitive processes across different operational domains. We provide a framework to disentangle those neural mechanisms that underpin the relationship between task demand, arousal, mental workload and human performance. This approach advocates targeting those specific mental states that precede a reduction of performance efficacy. A number of undesirable neurocognitive states (mind wandering, effort withdrawal, perseveration, inattentional phenomena) are identified and mapped within a two-dimensional conceptual space encompassing task engagement and arousal. We argue that monitoring the prefrontal cortex and its deactivation can index a generic shift from a nominal operational state to an impaired one where performance is likely to degrade. Neurophysiological, physiological and behavioral markers that specifically account for these states are identified. We then propose a typology of neuroadaptive countermeasures to mitigate these undesirable mental states.

Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations

The brain is the most important organ of the human body, and the conversations between the brain and an apparatus can not only reveal a normally functioning or a dysfunctional brain but also can modulate the brain. Here, the apparatus may be a nonbiological instrument, such as a computer, and the consequent brain-computer interface is now a very popular research area with various applications. The apparatus may also be a biological organ or system, such as the gut and muscle, and their efficient conversations with the brain are vital for a healthy life. Are there any common bases that bind these different scenarios? Here, we propose a new comprehensive cross area: Bacomics, which comes from brain-apparatus conversations (BAC) + omics. We take Bacomics to cover at least three situations: (1) The brain is normal, but the conversation channel is disabled, as in amyotrophic lateral sclerosis. The task is to reconstruct or open up new channels to reactivate the brain function. (2) The brain is in disorder, such as in Parkinson's disease, and the work is to utilize existing or open up new channels to intervene, repair and modulate the brain by medications or stimulation. (3) Both the brain and channels are in order, and the goal is to enhance coordinated development between the brain and apparatus. In this paper, we elaborate the connotation of BAC into three aspects according to the information flow: the issue of output to the outside (BAC-1), the issue of input to the brain (BAC-2) and the issue of unity of brain and apparatus (BAC-3). More importantly, there are no less than five principles that may be taken as the cornerstones of Bacomics, such as feedforward and feedback control, brain plasticity, harmony, the unity of opposites and systems principles. Clearly, Bacomics integrates these seemingly disparate domains, but more importantly, opens a much wider door for the research and development of the brain, and the principles further provide the general framework in which to realize or optimize these various conversations.

Optimizing Computer-Brain Interface Parameters for Non-invasive Brain-to-Brain Interface

A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain-computer interface (BCI) and the computer-brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were used to simulate a two-class medical monitoring BBI system under a wide range of conditions. BBI function was assessed using the information transfer rate (ITR), measured in bits per trial and bits per minute. The BBI ITR was a function of classifier accuracy, window update rate, system latency, stimulation failure rate (SFR), and timeout threshold. The BCI parameters, including window length, update rate, and classifier accuracy, were kept constant to investigate the effects of varying the CBI parameters, including system latency, SFR, and timeout threshold. Based on passively monitoring BCI parameters, a base ITR of 1 bit/trial was used. The optimal latency was found to be 100 ms or less, with a threshold no more than twice its value. With the optimal latency and timeout parameters, the system was able to maintain near-maximum efficiency, even with a 25% SFR. When the CBI and BCI parameters are compared, the CBI's system latency and timeout threshold should be reflected in the BCI's update rate. This would maximize the number of trials, even at a high SFR. These findings suggested that a higher number of trials per minute optimizes the ITR of a non-invasive BBI. The delays innate to each BCI protocol and CBI stimulation method must also be accounted for. The high latencies in each are the primary constraints of non-invasive BBI for the foreseeable future.

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.

Cognitive function assessment during 2 mA transcranial direct current stimulation in DLPFC in healthy volunteers

Although cognitive function has been reported to change following the anodal transcranial direct current stimulation (tDCS) but still variable results have been reported in healthy subject and there is paucity of data on the cognitive effects of online tDCS. Therefore, we aimed to assess the online effect of tDCS over the left dorsolateral prefrontal cortex (DLPFC) on cognitive function and obtain safety data in healthy adults. We recruited 36 healthy (20 male) participants for this double-blind, sham-controlled parallel design. We used Stop Signal Task (SST) Go Trial and Pattern Recognition Memory (PRM) tests to evaluate cognitive function during 2 mA (20 min) anodal or sham tDCS stimulation over the left DLPFC. In active conditions, left dorsolateral prefrontal cortex was selected for electrode placement with reference over right supraorbital cortex. All related tasks were done during the online tDCS section in both groups (active/sham). There were statistically significant differences in cognitive function according to the PRM test (P = 0.003), SST (P = 0.021), and SST correct response time on Go Trials (P = 0.02) during active stimulation compared to the sham group. Our results reveal that cognitive performance is affected by a single dose of active online tDCS over DLPFC area compared to sham stimulation. In our study, tDCS is well-tolerated and safe that further supports the safety of tDCS in local healthy population.

Collaborative Brain-Computer Interfaces to Enhance Group Decisions in an Outpost Surveillance Task

We present a two-layered collaborative Brain-Computer Interface (cBCI) to aid groups making decisions under time constraints in a realistic video surveillance setting - the very first cBCI application of this type. The cBCI first uses response times (RTs) to estimate the decision confidence the user would report after each decision. Such an estimate is then used with neural features extracted from EEG to refine the decision confidence so that it better correlates with the correctness of the decision. The refined confidence is then used to weigh individual responses and obtain group decisions. Results obtained with 10 participants indicate that cBCI-assisted groups are significantly more accurate than groups using standard majority or weighing decisions using reported confidence values. This two-layer architecture allows the cBCI to not only further enhance group performance but also speed up the decision process, as the cBCI does not have to wait for all users to report their confidence after each decision.