Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads.Methods: Seven ovine subjects were implanted with deep brain stimulation leads at different nodes within the Circuit of Papez: four with unilateral leads in (...) the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S® or Percept PC°ledR deep brain stimulation and recording device. Spontaneous local field potentials, evoked potentials, LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects.Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network.Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up. (shrink)

Recent advancements in the brain sciences have enabled researchers to determine, with increasing accuracy, patterns and locations of neural activation associated with various psychological functions. These techniques have revived a longstanding debate regarding the relation between the mind and the brain: while many authors claim that neuroscientific data can be employed to advance theories of higher cognition, others defend the so-called ‘autonomy’ of psychology. Settling this significant issue requires understanding the nature of the bridge laws used at the psycho- (...) class='Hi'>neural interface. While these laws have been the topic of extensive discussion, such debates have mostly focused on a particular type of link: reductive laws. Reductive laws are problematic: they face notorious philosophical objections and they are too scarce to substantiate current research at the intersection of psychology and neuroscience. The aim of this article is to provide a systematic analysis of a different kind of bridge laws—associative laws—which play a central, albeit overlooked role in scientific practice. (shrink)

Recent advancements in the brain sciences have enabled researchers to determine, with increasing accuracy, patterns and locations of neural activation associated with various psychological functions. These techniques have revived a longstanding debate regarding the relation between the mind and the brain: while many authors now claim that neuroscientific data can be used to advance our theories of higher cognition, others defend the so-called `autonomy' of psychology. Settling this significant question requires understanding the nature of the bridge laws used at (...) the psycho-neural interface. While these laws have been the topic of extensive discussion, such debates have mostly focused on a particular type of link: reductive laws. Reductive laws are problematic: they face notorious philosophical objections and they are too scarce to substantiate current research at the interface of psychology and neuroscience. The aim of this article is to provide a systematic analysis of a different kind of bridge laws--associative laws--which play a central, albeit often overlooked, role in scientific practice. (shrink)

Scientists, engineers, and healthcare professionals are currently developing a variety of new devices under the category of brain-computer interfaces (BCIs). Current and future applications are both medical/assistive (e.g., for communication) and non-medical (e.g., for gaming). This array of possibilities comes with ethical challenges for all stakeholders. As a result, BCIs have been an object of both hope and concern in various media. We argue that these conflicting sentiments can be productively understood in terms of personhood, specifically the impact of (...) BCIs on what it means to be a person and to be recognized as such by others. To understand the dynamics of personhood in the context of BCI use and investigate whether ethical guidance is required, a meeting entitled "BCIs and Personhood: A Deliberative Workshop" was held in May 2018. In this article, we describe how BCIs raise important questions about personhood and propose recommendations for BCI development and governance. (shrink)

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

Functional near-infrared spectroscopy has attracted increasing attention in the field of brain–computer interfaces owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network -based approach for implementing a subject-independent fNIRS-based BCI. (...) A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication as well as that obtained using conventional shrinkage linear discriminant analysis. To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly. (shrink)

An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role (...) of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design. (shrink)

Science fiction is fast becoming reality as scientists and engineers seek to develop new ways of directly accessing and controlling our brains through brain-computer and even brain-to-brain interfaces. If such research is to receive continuing public approval and support—and not invite opposition—it must anticipate the special ethical challenges it creates. By pointing to some of the acute concerns raised by neural engineering technologies—around issues of identity, normality, authority, responsibility, privacy, and justice—Eran Klein and colleagues model and stimulate the (...) kind of reflection that will be needed as new brain technologies are developed. And yet, as I read their informative discussion, I find myself asking even more questions. One concerns the ethical novelty of these technologies. For example, in terms of questions of identity, normality, responsibility, and authority, how different is deep brain stimulation from the potent psychiatric drugs that have been administered for decades? How different in terms of issues of liability associated with malfunction or misuse are brain-computer interfaces from other complex devices now routinely in use? Neither ethics nor law will have to rethink things from scratch as new neural devices and capabilities are introduced: the groundwork for answering some of these questions has already been laid in a variety of fields from bioethics, to health and product liability law. Nevertheless, I conclude that there are new questions here. It is not simply the fact that neural technologies pose questions of identity, privacy, and the like but that they do so with a degree of intensity that creates qualitatively new challenges. (shrink)

We present a neural dynamic model that perceptually grounds nested noun phrases, that is, noun phrases that contain further (possibly also nested) noun phrases as parts. The model receives input from the visual array and a representation of a noun phrase from language processing. It organizes a search for the denoted object in the visual scene. The model is a neural dynamic architecture of interacting neural populations which has clear interfaces with perceptual processes. It solves a (...) set of theoretical challenges, including the problem of keeping a nested structure in short-term memory in a way that solves the problem of 2 and massive binding problem emphasized by Jackendoff. The model organizes a search for the objects that are referenced in that structure. We motivate the model, demonstrate simulation results, and discuss how it differs from related models. (shrink)

Brain–computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are potentially vulnerable to cybercriminality. This opens the prospect of “neurocrime”: extending the range of computer-crime to neural devices. This paper explores a type of neurocrime that we call brain-hacking as it aims at the illicit access to and (...) manipulation of neural information and computation. As neural computation underlies cognition, behavior and our self-determination as persons, a careful analysis of the emerging risks of malicious brain-hacking is paramount, and ethical safeguards against these risks should be considered early in design and regulation. This contribution is aimed at raising awareness of the emerging risk of malicious brain-hacking and takes a first step in developing an ethical and legal reflection on those risks. (shrink)

Augmented reality-based brain–computer interface (AR–BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR–SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR–P300). SepCNN achieved single extraction of AR–P300 features and improved the recognition speed. A nine-target AR–P300 single-stimulus paradigm was designed (...) to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR–P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging. (shrink)

The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused (...) on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches. (shrink)

Brain–computer interfacing (BCI) aims at directly capturing brain activity in order to enable a user to drive an application such as a wheelchair without using peripheral neural or motor systems. Low signal to noise ratio’s, low processing speed, and huge intra- and inter-subject variability currently call for the addition of intelligence to the applications, in order to compensate for errors in the production and/or the decoding of brain signals. However, the combination of minds and machines through BCI’s and intelligent (...) devices (IDs) can affect a user’s sense of agency. Particularly confusing cases can arise when the behavioral control switches implicitly from user to ID. I will suggest that in such situations users may be insecure about the extent to which the resulting behavior, whether successful or unsuccessful, is genuinely their own. Hence, while performing an action, a user of a BCI–ID may be uncertain about being the agent of the act. Several cases will be examined and some implications for (legal) responsibility (e.g. establishing the presence of a ‘guilty mind’) are discussed. (shrink)

Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at the 6-month follow-up. Therefore, the clinical improvement and the neurological changes before, immediately after, (...) and 6 months after training were explored in 14 chronic stroke subjects. The upper-limb motor function was assessed by Action Research Arm Test (ARAT) and Fugl-Meyer Assessment for Upper-Limb (FMA), and the neurological changes were assessed using resting-state functional magnetic resonance imaging. Repeated-measure ANOVAs indicated that long-term motor improvement was found by both FMA (F[2,26]= 6.367,p= 0.006) and ARAT (F[2,26]= 7.230,p= 0.003). Seed-based functional connectivity analysis exhibited that significantly modulated FC was observed between ipsilesional motor regions (primary motor cortex and supplementary motor area) and contralesional areas (supplementary motor area, premotor cortex, and superior parietal lobule), and the effects were sustained after 6 months. The fALFF analysis showed that local neuronal activities significantly increased in central, frontal and parietal regions, and the effects were also sustained after 6 months. Consistent results in FC and fALFF analyses demonstrated the increase of neural activities in sensorimotor and fronto-parietal regions, which were highly involved in the BCI-guided training.Clinical Trial Registration:This study has been registered atClinicalTrials.govwith clinical trial registration number NCT02323061. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming (...) module associates category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)

According to the parallel architecture, syntactic and semantic information processing are two separate streams that interact selectively during language comprehension. While considerable effort is put into psycho- and neurolinguistics to understand the interchange of processing mechanisms in human comprehension, the nature of this interaction in recent neural Large Language Models remains elusive. In this article, we revisit influential linguistic and behavioral experiments and evaluate the ability of a large language model, GPT-3, to perform these tasks. The model can solve (...) semantic tasks autonomously from syntactic realization in a manner that resembles human behavior. However, the outcomes present a complex and variegated picture, leaving open the question of how Language Models could learn structured conceptual representations. (shrink)

In the recent literature at the interface between economics, biology and neuroscience, several authors argue that by adopting an interdisciplinary approach to the analysis of decision making, economists will be able to construct predictively and explanatorily superior models. However, most economists remain quite reluctant to import biological or neural insights into their account of choice behaviour. In this paper, I reconstruct and critique one of the main arguments by means of which economists attempt to vindicate their conservative position. Furthermore, (...) I develop an alternative defense of the thesis that economists justifiably rely on a methodologically distinctive approach to the modelling of choice behaviour. (shrink)

A brain-computer interface technology that can decode the neural signals associated with attempted but unarticulated speech could offer a future efficient means of communication for people with severe motor impairments. Recent demonstrations have validated this approach. Here we assume that it will be possible in future to decode imagined (i.e., attempted but unarticulated) speech in people with severe motor impairments, and we consider the characteristics that could maximize the social utility of a BCI for communication. As a social interaction, (...) communication involves the needs and goals of both speaker and listener, particularly in contexts that have significant potential consequences. We explore three high-consequence legal situations in which neurally-decoded speech could have implications:Testimony, where decoded speech is used as evidence;Consent and Capacity, where it may be used as a means of agency and participation such as consent to medical treatment; andHarm, where such communications may be networked or may cause harm to others. We then illustrate how design choices might impact the social and legal acceptability of these technologies. (shrink)

Neural engineering technologies such as implanted deep brain stimulators and brain-computer interfaces represent exciting and potentially transformative tools for improving human health and well-being. Yet their current use and future prospects raise a variety of ethical and philosophical concerns. Devices that alter brain function invite us to think deeply about a range of ethical concerns—identity, normality, authority, responsibility, privacy, and justice. If a device is stimulating my brain while I decide upon an action, am I still the author (...) of the action? Does a device make the interiority of my experience accessible to others? Will the device change the way I think of myself and others think of me? Such fundamental questions arise even when a device is designed for only a relatively circumscribed purpose, such as restoring functioning via a smart prosthetic. We are part of a National Science Foundation-funded Engineering Research Center tasked with investigating philosophical and social implications of neural engineering research and technologies. Neural devices already in clinical use, such as deep brain stimulators for Parkinson's disease or essential tremor, have spurred healthy debate about such implications. Devices currently under development—such as the BrainGate System of implanted brain sensors coupled to robotics in persons with paralysis, exoskeletons for augmented movement, transcranial do-it-yourself stimulators, closed-loop brain stimulating systems, or even brain-to-brain interfacing—promise to extend and deepen these debates. At our center, brain-computer interfaces are the principal focus of work. Even acknowledging that the clinical translation of neural devices and seamless integration by end users may still largely reside in the future, the potential these devices hold calls for careful early analysis. The launching of the Brain Research through Advancing Innovative Neurotechnologies Initiative in April 2013 provides further impetus for this work. (shrink)

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4–13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve (...) human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance. (shrink)

A thought experiment focuses attention on the kinds of commonalities and differences to be found in two small parts of visual cortical areas during responses to stimuli that are either identical in quality, but different in location, or identical in location and different only in the one visible property of colour. Reflection on this thought experiment leads to the view that patterns of neural activation are the best candidates for causes of qualitatively conscious events . This view faces a (...) strong objection, namely, that patterns can be realized in many media, and thus candidates for patterns that cause qualia might be realized in ways that would not plausibly result in consciousness. It is argued that this objection can be overcome if qualia-causing patterns of events must be realized within small spatial and temporal regions. Much more importantly, it is argued that this restriction on region size need not be ad hoc. The key concept needed to establish this important point is ‘natural salience’, i.e., distinction from background noise that does not depend on application of a criterion of selection. It is explained how natural salience could figure in an empirically-based theory that would entail size restrictions for qualia-causing neural activation patterns. The question is then raised as to how the resulting view diverges from Chalmers’ account, which relies on the Principle of Organizational Invariance. A second thought experiment envisages replacement of neurons by computer chips with synaptic interfaces. Reflection on this thought experiment enables us to conceptually, and possibly empirically, separate the two views. An argument for preferring the patterns- as-causes , or PACQ, view is given. Because natural salience does not plausibly produce strictly discontinuous boundaries between pattern and noise, questions naturally arise as to the relation of the PACQ view to panpsychism and to ‘emergence’. The PACQ view is distinguished from panpsychism, and it is explained how the former avoids what Seager calls ‘the combination problem', and is thus preferable to panpsychism. The relation of the PACQ view to ‘emergence’ is explained. The conclusion of the paper is that the PACQ view is a philosophically defensible and potentially scientifically fruitful view that offers qualia realists the best hypothesis concerning the neural causes of qualia. (shrink)

A brain-computer interface designed to restore motor function detects neural activity related to intended movement and thereby enables a person to control an external device, for example, a robotic limb, or even their own body. It would seem legitimate, therefore, to describe a BCI as a system that translates thought into action. This paper argues that present BCI-mediated behavior fails to meet the conditions of intentional physical action as proposed by causal and non-causal theories of action. First, according to (...) the causal theory of action physical actions are bodily movements that are causally related to a person’s intentions. It can be argued, however, that the proximate cause of action in present BCI-mediated behavior is not the person’s intention, and that the behavior fails to meet the conditions of reliability, sensitivity and difference-making. Second, BCI-mediated behavior can be accommodated by a Volitionist account of action if we can equate imagining movement with trying to move. The argument presented is that the novelty, and limited functionality and sensory feedback of present BCIs challenges this equation. (shrink)

Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive (...) control, with a focus on the influence that the PDP approach has brought to bear in this area. Rather than providing a comprehensive review, we offer a framework for thinking about past and future modeling efforts in this domain. We define control in terms of the optimal parameterization of task processing. From this vantage point, the development of control systems in the brain can be seen as responding to the structure of naturalistic tasks, through the filter of the brain systems with which control directly interfaces. This perspective lays open a set of fascinating but difficult research questions, which together define an important frontier for future computational research. (shrink)

This paper reviews a series of experimental studies that address what we call ‘interface judgement’, which is the complex judgment involving integration from multiple levels of grammatical representation such as the syntax-semantics and prosody-semantics interface. We first discuss the results from the ERP literature connected to NPI licensing in different languages, paying particular attention to the N400 and the P600 as neural correlates of this specific phenomenon and focusing on the study by Xiang et al. (2016). The results of (...) this study show evidence that there are two distinct NPI licensing mechanisms, i.e., licensing and rescuing, in line with Giannakidou (1998, 2006). Then we discuss an acceptability judgement task on Greek NPIs which supports the negativity as a scale hypothesis (Zwarts 1995, 1996, Giannakidou 1998). For the semantics-prosody interface judgment, we discuss two types of findings on two different phenomena and languages: (i) the study by Giannakidou & Yoon (2016) on scalar and non-scalar NPIs in Greek and Korean, which serves as the foundation for Chatzikontantinou (2016)’s study of production data showing distinct prosodic properties in emphatic (scalar) and non-emphatic (non-scalar) Greek NPIs; (ii) a (production and perception) study by Etxeberria & Irurtzun (2015) on the prosodic disambiguation of the scalar/non-scalar readings of sentences containing the focus particle ‘ere’ in Basque. The main conclusion of the paper is that experimental methods of the kind discussed in the paper are useful in establishing physical, quantitative correlates of interface judgement. (shrink)

Convolutional neural networks, which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic signals, especially for decoding brain states in brain-computer interfaces. Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the (...) effects of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales in an efficient and optimized way, and was validated on three P300 datasets. The CNN was trained using different strategies and was compared with several state-of-the-art algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs. (shrink)

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. (shrink)

Brain–Computer Interface research is an interdisciplinary area of study within Neural Engineering. Recent interest in end-user perspectives has led to an intersection with user-centered design. The goal of user-centered design is to reduce the translational gap between researchers and potential end users. However, while qualitative studies have been conducted with end users of BCI technology, little is known about individual BCI researchers’ experience with and attitudes towards UCD. Given the scientific, financial, and ethical imperatives of UCD, we sought to (...) gain a better understanding of practical and principled considerations for researchers who engage with end users. We conducted a qualitative interview case study with neural engineering researchers at a center dedicated to the creation of BCIs. Our analysis generated five themes common across interviews. The thematic analysis shows that participants identify multiple beneficiaries of their work, including other researchers, clinicians working with devices, device end users, and families and caregivers of device users. Participants value experience with device end users, and personal experience is the most meaningful type of interaction. They welcome end-user input, but are skeptical of limited focus groups and case studies. They also recognize a tension between creating sophisticated devices and developing technology that will meet user needs. Finally, interviewees espouse functional, assistive goals for their technology, but describe uncertainty in what degree of function is “good enough” for individual end users. Based on these results, we offer preliminary recommendations for conducting future UCD studies in BCI and neural engineering. (shrink)

The loss or absence of vision is probably one of the most incapacitating events that can befall a human being. The importance of vision for humans is also reflected in brain anatomy as approximately one third of the human brain is devoted to vision. It is therefore unsurprising that throughout history many attempts have been undertaken to develop devices aiming at substituting for a missing visual capacity. In this review, we present two concepts that have been prevalent over the last (...) two decades. The first concept is sensory substitution, which refers to the use of another sensory modality to perform a task that is normally primarily sub-served by the lost sense. The second concept is cross-modal plasticity, which occurs when loss of input in one sensory modality leads to reorganization in brain representation of other sensory modalities. Both phenomena are training-dependent. We also briefly describe the history of blindness from ancient times to modernity, and then proceed to address themeansthat have been used to help blind individuals, with an emphasis on modern technologies, invasive (various type of surgical implants) and non-invasive devices. With the advent of brain imaging, it has become possible to peer into the neural substrates of sensory substitution and highlight the magnitude of the plastic processes that lead to a rewired brain. Finally, we will address the important question of the value and practicality of the available technologies and future directions. (shrink)

Neurodevices that collect neural (or brain activity) data have been characterised as having the ability to register the inner workings of human mentality. There are concerns that the proliferation of such devices in the consumer-directed realm may result in the mass processing and commercialisation of neural data (as has been the case with social media data) and even threaten the mental privacy of individuals. To prevent this, some argue that all raw neural data should be conceptualised and (...) regulated as “medical data” even if it is collected from consumer-directed devices in obviously non-clinical settings. In this paper, we argue that without a clearer formulation of what does and does not count as medical data, this approach might also uncritically enlarge the scope for medical influence and an unwarranted medicalisation of everyday aspects of mental life. Indeed, if we were to accept the position that all neural data is medical data because it offers insights into personally sensitive information (such a person’s thoughts, emotions, or intentions) then this could even unnecessarily expand the boundaries of medical data to other forms of data that otherwise seem to be non-clinical. If all brain data (neural activity data) is considered to be medical data even when collected from consumer-directed devices then we might unintentionally reduce meaningful distinctions between what is and what is not rightfully in the purview of medicine. (shrink)

The computer distance teaching system teaches through the network, and there is no entrance threshold. Any student who is willing to study can log in to the network computer distance teaching system for study at any free time. Neural network has a strong self-learning ability and is an important part of artificial intelligence research. Based on this study, a neural network-embedded architecture based on shared memory and bus structure is proposed. By looking for an alternative method of exp (...) function to improve the speed of radial basis function algorithm, and then by analyzing the judgment conditions in the main loop during the algorithm process, these judgment conditions are modified conditionally to reduce the calculation scale, which can double the speed of the algorithm. Finally, this article verifies the function, performance, and interface of the computer distance education system. (shrink)

This paper presents an in-depth study and analysis of the emotional classification of EEG neurofeedback interactive electronic music compositions using a multi-brain collaborative brain-computer interface (BCI). Based on previous research, this paper explores the design and performance of sound visualization in an interactive format from the perspective of visual performance design and the psychology of participating users with the help of knowledge from various disciplines such as psychology, acoustics, aesthetics, neurophysiology, and computer science. This paper proposes a specific mapping model (...) for the conversion of sound to visual expression based on people’s perception and aesthetics of sound based on the phenomenon of audiovisual association, which provides a theoretical basis for the subsequent research. Based on the mapping transformation pattern between audio and visual, this paper investigates the realization path of interactive sound visualization, the visual expression form and its formal composition, and the aesthetic style, and forms a design expression method for the visualization of interactive sound, to benefit the practice of interactive sound visualization. In response to the problem of neglecting the real-time and dynamic nature of the brain in traditional brain network research, dynamic brain networks proposed for analyzing the EEG signals induced by long-time music appreciation. During prolonged music appreciation, the connectivity of the brain changes continuously. We used mutual information on different frequency bands of EEG signals to construct dynamic brain networks, observe changes in brain networks over time and use them for emotion recognition. We used the brain network for emotion classification and achieved an emotion recognition rate of 67.3% under four classifications, exceeding the highest recognition rate available. (shrink)

The brain-computer interface of steady-state visual evoked potential is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density, we perform zero-padding in the signal's time domain to improve its performance on the PSD and make (...) it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI. (shrink)

Brain-computer interfaces allow agents to control computers without moving their bodies. The agents imagine certain things and the brain-computer interfaces read the concomitant neural activity and operate the computer accordingly. But the use of brain-computer interfaces is problematic for criminal law, which requires that someone can only be found criminally responsible if they have satisfied the actus reus requirement: that the agent has performed some (suitably specified) conduct. Agents who affect the world using brain-computer interfaces (...) do not obviously perform any conduct, so when they commit crimes using brain-computer interfaces it is unclear how they have satisfied actus reus. Drawing on a forthcoming paper by Allan McCay, I suggest three potential accounts of the conduct that satisfies actus reus: the agent’s neural firings, his mental states, and the electronic activity in his brain-computer interface. I then present two accounts which determine how actus reus may be satisfied – one a counterfactual and the other a minimal sufficiency account. These accounts are lent plausibility because they are analogous to the but-for and NESS (Necessary Element in a Sufficient Set) tests for causation which are generally accepted tests for causation in legal theory. I argue that due to the determinations of these accounts and considerations regarding the relationship between the mind and brain, actus reus is satisfied by either the agent’s neural activity or brain-computer interface electrical activity. Which of these satisfies actus reus is determined by how well the brain-computer interface is functionally integrated with the agent. (shrink)

Neural engineers and clinicians are starting to translate advances in electrodes, neural computation, and signal processing into clinically useful devices to allow control of wheelchairs, spellers, prostheses, and other devices. In the process, large amounts of brain data are being generated from participants, including intracortical, subdural and extracranial sources. Brain data is a vital resource for BCI research but there are concerns about whether the collection and use of this data generates risk to privacy. Further, the nature of (...) BCI research involves understanding and making inferences about device users’ mental states, thoughts, and intentions. This, too, raises privacy concerns by providing otherwise unavailable direct or privileged access to individuals mental lives. And BCI-controlled prostheses may change the way clinical care is provided and the type of physical access caregivers have to patients. This, too, has important privacy implications. I In this chapter we examine several of these privacy concerns in light of prominent views of the nature and value of privacy. We argue that increased scrutiny needs to be paid to privacy concerns arising from Big Data and decoding of mental states, but that BCI research may also provide opportunity for individuals to enhance their privacy. (shrink)

We present a dynamical model of interaction between recognition memory and affect, focusing on the phenomenon of “warm glow of familiarity.” In our model, both familiarity and affect reflect quick monitoring of coherence in an attractor neural network. This model parsimoniously explains a variety of empirical phenomena, including mere-exposure and beauty-in-averages effects, and the speed of familiarity and affect judgments.

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective (...) of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number (...) of subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

By emphasizing nonlinear dynamics between appraisal and emotions, Lewis's model provides a valuable platform for integrating psychological and neural perspectives on the emotion-cognition interface. In this commentary, I discuss the role of neuroscience in shaping new conceptualizations of emotion and the putative role of theta oscillation within frontocingulate pathways in depression, a syndrome in which emotion-cognition relations are dysfunctional.

University Abstract Philosophers have sought to improve upon the logical empiricists’ model of scientific reduction. While opportunities for integration between the cognitive and the neural sciences have increased, most philosophers, appealing to the multiple realizability of mental states and the irreducibility of consciousness, object to psychoneural reduction. New Wave reductionists offer a continuum of comparative goodness of intertheoretic mapping for assessing reductions. Their insistence on a unified view of intertheoretic relations obscures epistemically significant crossscientific relations and engenders dismissive conclusions (...) about psychology. Richer, more sensitive accounts of explanatory pluralism and mechanistic explanation in science advocate multi-level approaches in cross-scientific settings and criticize the distance of the standard philosophical objections from working scientists’ practices and discoveries. The Heuristic Identity Theory, a new, scientifically informed version of the psycho-physical identity theory, incorporates these insights, showing how multiple realizability is an argument for (not against) identities in science and why, therefore, consciousness is not irreducible. (shrink)

Motor imagery-based brain-computer interfaces have been studied without controlling subjects’ gaze fixation position previously. The effect of gaze fixation and covert attention on the behavioral performance of BCI is still unknown. This study designed a gaze fixation controlled experiment. Subjects were required to conduct a secondary task of gaze fixation when performing the primary task of motor imagination. Subjects’ performance was analyzed according to the relationship between motor imagery target and the gaze fixation position, resulting in three BCI control (...) conditions, i.e., congruent, incongruent, and center cross trials. A group of fourteen subjects was recruited. The average group performances of three different conditions did not show statistically significant differences in terms of BCI control accuracy, feedback duration, and trajectory length. Further analysis of gaze shift response time revealed a significantly shorter response time for congruent trials compared to incongruent trials. Meanwhile, the parietal occipital cortex also showed active neural activities for congruent and incongruent trials, and this was revealed by a contrast analysis of R-square values and lateralization index. However, the lateralization index computed from the parietal and occipital areas was not correlated with the BCI behavioral performance. Subjects’ BCI behavioral performance was not affected by the position of gaze fixation and covert attention. This indicated that motor imagery-based BCI could be used freely in robotic arm control without sacrificing performance. (shrink)

Assistive systems based on Artificial Intelligence (AI) are bound to reshape decision-making in all areas of society. One of the most intricate challenges arising from their implementation in high-stakes environments such as medicine concerns their frequently unsatisfying levels of explainability, especially in the guise of the so-called black-box problem: highly successful models based on deep learning seem to be inherently opaque, resisting comprehensive explanations. This may explain why some scholars claim that research should focus on rendering AI systems understandable, rather (...) than explainable. Yet, there is a grave lack of agreement concerning these terms in much of the literature on AI. We argue that the seminal distinction made by the philosopher and physician Karl Jaspers between different types of explaining and understanding in psychopathology can be used to promote greater conceptual clarity in the context of Machine Learning (ML). Following Jaspers, we claim that explaining and understanding constitute multi-faceted epistemic approaches that should not be seen as mutually exclusive, but rather as complementary ones as in and of themselves they are necessarily limited. Drawing on the famous example of Watson for Oncology we highlight how Jaspers’ methodology translates to the case of medical AI. Classical considerations from the philosophy of psychiatry can therefore inform a debate at the centre of current AI ethics, which in turn may be crucial for a successful implementation of ethically and legally sound AI in medicine. (shrink)

Electroencephalography and functional near-infrared spectroscopy have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework (...) consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery and mental arithmetic tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS. (shrink)

I join Gerben Meynen’s call for an ethical assessment of mind-reading technology by enlarging on four points he raises. First, I suggest distinguishing between neural and mental data, apprehending...

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective (...) of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

Recording and manipulating neuronal ensemble activity is a key requirement in advanced neuromodulatory and behavior studies. Devices capable of both recording and manipulating neuronal activity brain-computer interfaces should ideally operate un-tethered and allow chronic longitudinal manipulations in the freely moving animal. In this study, we designed a new intracortical BCI feasible of telemetric recording and stimulating local gray and white matter of visual neural circuit after irradiation exposure. To increase the translational reliance, we put forward a Göttingen minipig (...) model. The animal was stereotactically irradiated at the level of the visual cortex upon defining the target by a fused cerebral MRI and CT scan. A fully implantable neural telemetry system consisting of a 64 channel intracortical multielectrode array, a telemetry capsule, and an inductive rechargeable battery was then implanted into the visual cortex to record and manipulate local field potentials, and multi-unit activity. We achieved a 3-month stability of the functionality of the un-tethered BCI in terms of telemetric radio-communication, inductive battery charging, and device biocompatibility for 3 months. Finally, we could reliably record the local signature of sub- and suprathreshold neuronal activity in the visual cortex with high bandwidth without complications. The ability to wireless induction charging combined with the entirely implantable design, the rather high recording bandwidth, and the ability to record and stimulate simultaneously put forward a wireless BCI capable of long-term un-tethered real-time communication for causal preclinical circuit-based closed-loop interventions. (shrink)

This book offers a philosophical analysis of what it is to be a human being in all her aspects. It analyses what is meant by the self and the I and how this feeling of a self or an I is connected to the brain. It studies specific cases of brain disorders, based on the idea that in order to understand the common, one has to study the specific. The book shows how the self is thought of as a three-fold (...) emergent self, comprising a relationship between an objective neural segment, a subjective neural segment and a subjective transcendent segment. It explains that the self in the world tackles philosophical problems such as the problem of free will, the problem of evil, the problem of human uniqueness and empathy. It demonstrates how the problem of time also has its place here. For many people, the world includes ultimate reality; hence the book provides an analysis and evaluation of different relationships between human beings and Ultimate Reality (God). The book presents an answer to the philosophical problem of how one could understand divine action in the world. (shrink)

Recent findings in autopsy studies, neuroimaging, and neurophysiology indicate that dyslexia is accompanied by fundamental changes in brain anatomy and physiology, involving several anatomical and physiological stages in the processing stream, which can be attributed to anomalous prenatal and immediately postnatal brain development. Epidemiological evidence in dyslexic families led to the discovery of animal models with immune disease, comparable anatomical changes and learning disorders, which have added needed detail about mechanisms of injury and plasticity to indicate that substantial changes in (...) neural networks concerned with perception and cognition are present. It is suggested that the disorder of language, which is the cardinal finding in dyslexic subjects, results from early perceptual anomalies that interfere with the establishment of normal cognitive-linguistic structures, coupled with primarily disordered cognitive processing associated with developmental anomalies of cortical structure and brain asymmetry. This notion is supported by electrophysiological data and by findings of anatomical involvement in subcortical structures close to the input as well as cortical structures involved in language and other cognitive functions. It is not possible at present to determine where the initial insult lies, whether near the input or in high-order cortex, or at both sites simultaneously. (shrink)

This article describes two features of technoscience, which are significant for the consideration of the prospects of human improvement projects. The first feature of technoscience is that the object of its research is artificial in origin which means created by person. As an example of new objects, situations and problems are given projects to create «designer children», development of transplantation, creating implantable neural interface. The second feature of technoscience is that the well-established methods can't be applied to determine the (...) results of the implementation of human improvement projects. As an example, the article discussed neo-eugenics projects. The article concludes that today in the social sciences there is no clear position on the grounds of human improvement projects. However, this is not an obstacle for the development of technoscience. (shrink)