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

In “Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?” Daniel Susser and Laura Cabrera (2024) make a compelling case as to why the greater availability of neural data itsel...

The focus of this paper are the ethical, legal and social challenges for ensuring the responsible use of “big brain data”—the recording, collection and analysis of individuals’ brain data on a large scale with clinical and consumer-directed neurotechnological devices. First, I highlight the benefits of big data and machine learning analytics in neuroscience for basic and translational research. Then, I describe some of the technological, social and psychological barriers for securing brain data from (...) unwarranted access. In this context, I then examine ways in which safeguards at the hardware and software level, as well as increasing “data literacy” in society, may enhance the security of neurotechnological devices and protect the privacy of personal brain data. Regarding ethical and legal ramifications of big brain data, I first discuss effects on the autonomy, the sense of agency and authenticity, as well as the self that may result from the interaction between users and intelligent, particularly closed-loop, neurotechnological devices. I then discuss the impact of the “datafication” in basic and clinical neuroscience research on the just distribution of resources and access to these transformative technologies. In the legal realm, I examine possible legal consequences that arises from the increasing abilities to decode brain states and their corresponding subjective phenomenological experiences on the hitherto inaccessible privacy of these information. Finally, I discuss the implications of big brain data for national and international regulatory policies and models of good data governance. (shrink)

Where there is data there are questions of ownership, leaks, and worries about misuse. When what’s at stake is data on our brains, the stakes are high. This book brings together philosophical analysis and neuroscientific insights to develop an account of ‘brain data’: what it is, how it is used, and how we ought to take care of it. Emerging trends in neuroscience appear to make mental activity legible, through sophisticated processing of signals recorded from the (...) brain. This can include Artificial Intelligence (AI), with algorithms classifying brain signals for further processing. These developments will have ramifications for concepts of the brain, the self, and the mind. They will also affect clinical practices like psychiatry, by modifying concepts of mental health and introducing AI-based diagnostic and treatment strategies. The issues arising are vastly complicated, little understood, but of high importance. Philosophical Perspectives on Brain Data clarifies complex intersections of philosophical and neuroscientific interest, presenting an account of brain data that is comprehensible. This account can be the basis for evaluating practices based on brain data. As such, the book aims to open a novel space for evaluating hitherto arcane areas of academic research in order to provide the necessary scope for understanding their real-world consequences. These consequences will include personal, socio-political, and public health dimensions. It is therefore vital that they are understood if their impacts upon aspects of everyday life can be evaluated adequately. (shrink)

Neurotechnologies that collect and interpret data about brain activity are already in use for medical and nonmedical applications. Refinements of existing noninvasive techniques and the discovery of new ones will likely encourage broader uptake. The increased collection and use of brain data and, in particular, their use to infer the existence of mental states have led to questions about whether mental privacy may be threatened. It may be threatened if the brain data actually support (...) inferences about the mind or if decisions are made about a person in the belief that the inferences are justified. This article considers the chain of inferences lying between data about neural activity and a particular mental state as well as the ethico-legal issues raised by making these inferences, focusing here on what the threshold of reliability should be for using brain data to infer mental states. (shrink)

Open peer commentary on the target article “How and Why the Brain Lays the Foundations for a Conscious Self” by Martin V. Butz. Excerpt: Surprisingly, the paper entirely neglects the issue of the dynamic properties of our environment. Focusing on (static, inanimate) objects only, it fails to acknowledge that anticipation becomes especially relevant when things around us change without being under our control: this is when we are forced to adapt quickly to new circumstances. To estimate as precisely as (...) necessary what will when be where is of vital meaning. Surprisingly though, this issue is not addressed at all. Although it is acknowledged that the environment contains dynamics that are to be predicted somehow (§3) and that have to be represented in the brain somehow (§28), concepts about and empirical data on the prediction of external events are not dealt with further. (shrink)

There is a bias in neuroscience toward localizing and modularizing brain functions. Single cell recording, imaging studies, and the study of neurological deficits all feed into the Gallian view that different brain areas do different things and the things being done are confined to particular processing streams. At the same time, there is a growing sentiment that brains probably don’t work like that after all; it is better to conceive of them as fundamentally distributed units, multi‐tasking at every (...) level. This sentiment, however, is much less congenial to the tried‐and‐true experimental protocols available today and to theorizing about the brain in general. This essay examines the tension between current experimental methods and large-scale views of the brain. We argue that this disconnection between experiment and what really are guiding theoretical metaphors seriously impedes progress in neuroscience. (shrink)

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

Contrary to received opinion among philosophers, psychologists, and neuroscientists, conscious duality as a principle of brain organization is neither incoherent nor demonstrably false. The present paper begins by reviewing the history of the theory and its anatomical basis and defending it against the claim that it rests upon an arbitrary decision as to what constitutes the biological substratum of mind or person.

Advances in neuroscience and other disciplines are producing large-scale brain data consisting of datasets from multiple organisms, disciplines, and jurisdictions in different formats. However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and legal principles which may influence key stakeholders involved in the generation, collection, processing and sharing of brain data thereby raising ethical and legal challenges. In addition, despite the demand (...) for a brain data governance framework that accounts for culture, there is a gap in empirical research and actions to understand how key stakeholders around the world view these issues using neuroscientists who are affected by these ethical and legal principles. Therefore, using the research question how do ethical and legal principles influence data governance in neuroscience? we attempt to understand the perceptions of key actors on the principles, issues and concerns that can arise from brain data research. We carried out interviews with 21 leading international neuroscientists. The analytical insights revealed key ethical and legal principles, areas of convergence, visibility, and the contextual issues and concerns that arise in brain data research around these principles. These issues and concerns circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. Also, key contextual insights around animal research and ethics were identified. The research identifies key principles, issues, and concerns that need to be addressed in advancing the development of a framework for global brain data governance. By presenting contextual insights from neuroscientists across regions, the study contributes to informing discussions and shaping policies aimed at promoting responsible and ethical practices in brain data research. The research answers the call for a cross cultural study of global brain data governance and the results of the study will assist in understanding the issues and concerns that arise in brain data governance. (shrink)

This paper traces the dramatic proliferation of leadership roles in English primary and secondar schools, due mainly to central government education policy of the past two decades. This has transformed schools from relatively simple to highly complex organizations and has impacted on the working conditions of, and demands on, teachers, together with many aspects of schooling. These changes are illustrated with typical examples of schools' leadership structures and their functioning. Interview data provide teachers' views on, and reactions to, the (...) changes in school leadership. The paper also reviews the ways in which teacher education institutions have responded in terms of providing initial and in-service education and training to equip the profession for this new and developing challenge. It examines the reception of such programmes by teachers and the reported impact on schools' management and the role of leadership within it. (shrink)

Advances in neurotechnologies, artificial intelligence (AI) and Big Data analytics are allowing interpretation of patterns from brain data to identify and even predict and manipulate mental states. Furthermore, there are avenues through which brain data can move into the consumer sphere, be reidentified and brokered. In response to these developments, there have been a number of approaches proposed to strengthen protections of brain data. To better understand the landscape of brain data (...) protection discussions, we conducted a scoping review to identify the rationales for establishing brain data protections and the proposals for protecting brain data offered in the ethics and neuroscience literature. To draw comparisons, we also surveyed the rationales given in the literature for the protection of sensitive behavioral inferences drawn from other types of personal data and associated approaches to achieving protection. This systematic examination of the rationale behind heightened protection for brain data should be useful to clarify the functional and conceptual bases given for brain data protection and to provide a better grounding for evaluating how the different approaches to brain data protection address these concerns. (shrink)

The world of work is being changed at an unprecedented rate as a result of the rise of the Fourth Industrial Revolution. This rate of change was accelerated by the COVID-19 pandemic, which left organizations and their leadership to deal with myriad of challenges. These changes also impacted leaders’ identities in their work and their roles in their organizations. We examine how leaders responded to the various workplace challenges presented by the COVID-19 pandemic and what this meant for their work (...) identities as leaders. To do this, we made use of role identity theory, social identity theory, and leader identity. A qualitative study was conducted with a group of eight senior leaders from various South African and global organizations who had between five and 10 years’ work experience, and some had even more. Data were collected through semi-structured interviews, conducted virtually and in person. Thematic analysis was used to analyze the data. The main finding that emerged from the research was that leaders employed virtual leadership to ensure that customers’ expectations were met, and to manage team-and organizational performance. These leaders achieved this by fostering a digital culture and building effective teams. They achieved their leadership goals by ensuring social identity continuity amongst their teams. This required them taking on extra roles, such as strategist, technology expert, entrepreneur, coach, mentor, and member of the team. Their leader role identity, as part of their work identity, was amplified by the pandemic. The implication is that organizations should develop leadership development programs to increase and strengthen leader identities to capacitate them for times of crisis. (shrink)

Regardless of formalization used, one on-going challenge for AI systems that model legal proceedings is accounting for contextual issues, particularly where judicial decisions are made in criminal cases. The law assumes a rational approach to rule application in deciding a defendant’s guilt; however, judges and juries can behave irrationally. What should a model prize: efficiency, accuracy, or fairness? Exactly whether and how to incorporate the psychology of courtroom interactions into formal models or expert systems has only just begun to be (...) examined in a serious fashion. Here, I outline data from the United States which suggest that trying to incorporate psychological biases into formal models of legal decision-making will be challenging. I focus on the use of neuroscience data in criminal trials, homing in on so-called group-to-individual inferences. I argue that data which should be the most effective at swaying judicial decisions are in fact those most likely not to make a difference in the disposition of the case. I conclude that judges often assign culpability by ignoring what our best science regarding how human decision-making occurs. (shrink)

We are frequently exposed to hand written digits 0-9 in today’s modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain-computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of fMRI (functional (...) Magnetic Resonance imaging) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25-30. Results in different brain lobes (frontal, occipital, parietal and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor and language processing), but the most relevant one to the task was language processing network according to the voxel selection. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I (...) argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

The expansion of research on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises important neuroethics and policy questions related to data sharing. However, there has been little empirical research on the perspectives of experts developing these technologies. We conducted semi-structured, open-ended interviews with aDBS researchers regarding their data sharing practices and their perspectives on ethical and policy issues related to sharing. Researchers expressed support for and a commitment to sharing, with most saying that they were either (...) sharing their data or would share in the future and that doing so was important for advancing the field. However, those who are sharing reported a variety of sharing partners, suggesting heterogeneity in sharing practices and lack of the broad sharing that would reflect principles of open science. Researchers described several concerns and barriers related to sharing, including privacy and confidentiality, the usability of shared data by others, ownership and control of data (including potential commercialization), and limited resources for sharing. They also suggested potential solutions to these challenges, including additional safeguards to address privacy issues, standardization and transparency in analysis to address issues of data usability, professional norms and heightened cooperation to address issues of ownership and control, and streamlining of data transmission to address resource limitations. Researchers also offered a range of views on the sensitivity of neural activity data (NAD) and data related to mental health in the context of sharing. These findings are an important input to deliberations by researchers, policymakers, neuroethicists, and other stakeholders as they navigate ethics and policy questions related to aDBS research. (shrink)

For an economist, ultimate goals of neuroeconomic research include improving economic policy analysis. One path toward this goal is to use neuroeconomic data to advance economic theory, and productive efforts have been made towards that end. Equally important, though less studied, is how neuroeconomics can provide quantitative evidence on policy, and in particular the way in which it might inform structural econometric inference. This paper is a first step in that direction. We suggest here that key forms of preference (...) (or decision strategy) heterogeneity can be identified by brain imaging studies and, consequently, linked stochastically to observable individual characteristics. Then, recognizing that brain-imaging studies are substantially costly, we derive conditions under which the probabilistic link between observable characteristics and type, a quantity critical to policy analysis, can be estimated more precisely by combining data from traditional and brain-based decision studies. (shrink)

Discussions about brain data and privacy, particularly those advocating for human rights frameworks, at times, have embodied problematic undercurrents of, if not overt appeals to, neuro-exceptional...

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)

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)

Ontologies formally represent reality in a way that limits ambiguity and facilitates automated reasoning and data fusion, but is often daunting to the non-technical user. Thus, many researchers have endeavored to hide the formal syntax and semantics of ontologies behind the constructs of Controlled Natural Languages (CNLs), which retain the formal properties of ontologies while simultaneously presenting that information in a comprehensible natural language format. In this paper, we build upon previous work in this field by evaluating prospects of (...) implementing International Technology Alliance Controlled English (ITACE) as a middleware for ontology editing. We also discuss at length a prototype of a natural language conversational interface application designed to facilitate ontology editing via the formulation of CNL constructs. (shrink)

As a special occupational group, the working and living environments faced by seafarers are greatly different from those of land. It is easy to affect the psychological and physiological activities of seafarers, which inevitably lead to changes in the brain functional activities of seafarers. Therefore, it is of great significance to study the neural activity rules of seafarers’ brain. In view of this, this paper studied the seafarers’ brain alteration at the activated voxel level based on functional (...) magnetic resonance imaging technology by comparing the differences in functional connectivities between seafarers and non-seafarers. Firstly, the activated voxels of each group were obtained by independence component analysis, and then the distribution of these voxels in the brain and the common activated voxels between the two groups were statistically analyzed. Next, the FCs between the common activated voxels of the two groups were calculated and obtained the FCs that had significant differences between them through two-sample T-test. Finally, all FCs and FCs with significant differences between the common activated voxels were used as the features for the support vector machine to classify seafarers and non-seafarers. The results showed that DFCs between the activated voxels had better recognition ability for seafarers, especially for Precuneus_L and Precuneus_R, which may play an important role in the classification prediction of seafarers and non-seafarers, so that provided a new perspective for studying the specificity of neurological activities of seafarers. (shrink)

In this series of eight articles, the Australian Traumatic Brain Injury Initiative (AUS-TBI) consortium describes the Australian approach used to select the common data elements collected acutely that have been shown to predict outcome following moderate-severe traumatic brain injury (TBI) across the lifespan. This article presents the unified single data dictionary, together with additional measures chosen to facilitate comparative effectiveness research and data linkage. Consultations with the AUS-TBI Lived Experience Expert Group provided insights on the (...) merits and considerations regarding data elements for some of the study areas, as well as more general principles to guide the collection of data and the selection of meaningful measures. These are presented as a series of guiding principles and themes. The AUS-TBI Aboriginal and Torres Strait Islander Advisory Group identified a number of key points and considerations for the project approach specific to Aboriginal and Torres Strait Islander peoples, including key issues of data sovereignty and community involvement. These are outlined in the form of principles to guide selection of appropriate methodologies, data management, and governance. Implementation of the AUS-TBI approach aims to maximize ongoing data collection and linkage, to facilitate personalization of care and improved outcomes for people who experience moderate-severe TBI. (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)

Laboratoire de Neurosciences Cognitives et Imagerie Ce´re´brale (LENA), Hoˆpital de La Salpeˆtrie`re, Centre National de la Recherche Scientifique (CNRS).

Data from magnetic resonance imaging, autopsy, endocranial measurements, and other techniques show that: brain size correlates 0.40 with cognitive ability; average brain size varies by race; and average cognitive ability varies by race. These results are as replicable as one will find in the social and behavioral sciences. They pose serious problems for Rose 's claim that reductionistic science is inadequate, inefficient, and/or unproductive.

Wright & Liley provide an advance in addressing the interaction of multiple scales of processing in the brain. It should address in more detail the biological evidence that underlies the models it proposes to replace.

Neuroscience has become increasingly reliant on multi-subject research in addition to studies of unusual single patients. This research has brought with it a challenge: how are data from different human brains to be combined? The dominant strategy for aggregating data across brains is what I call the ‘cartographic approach’, which involves mapping data from individuals to a spatial template. Here I characterize the cartographic approach and argue that one of its key steps, registration, should be carried out (...) in a way that is sensitive to the target of investigation. Because registration aims to align homologous brain locations, but not all homologous locations can be simultaneously aligned, a multiplicity of registration methods is required to meet the needs of researchers investigating different phenomena. I call this position ‘registration pluralism’. Registration pluralism has potential implications for neuroscientific practice, three of which I discuss here. This work shows the importance of reflecting more carefully on data aggregation methods, especially in light of the substantial individual differences that exist between brains. (shrink)

In the spring of 1987, I was in Havana, Cuba, where I was participating in planning a large-scale longitudinal study of the neurophysiological, neurochemical, and behavioral characteristics of cohorts of patients with cerebrovascular disease, depression, senile dementia, schizophrenia, or learning disabilities; and also part of this study were their first-degree blood relatives. This study was the outgrowth of a long-term project on the practical application of computer methods for the evaluation of brain electrical activity related to anatomical integrity, maturational (...) development, and sensory, perceptual;·-and cognitive processes, especially in chil dren. For many years, that project had been supported by the United Nations Development Program (UNDP), the National Scientific Research Center of Cuba (CNIC), and the Ministries of Public Health and of Education of Cuba. Since its inception, I had served as a technical advisor to the UNDP project. When the project began, I became acquainted with Dr. Jose M. Miyar Barrueco, who was at that time the Rector of the Medical School of the University of Havana. Because of his keen interest in the new computer technology and its potential utility in developing countries, we met from time to time during my visits. These occasional meetings continued after he became Secretary of the Cuban Council of State, so that he could remain apprised of progress and problems with which he might help. (shrink)

Parkinson´s disease (PD) is a neurodegenerative disorder characterized by motor symptoms following dopaminergic depletion in the substantia nigra. Besides motor impairments however, several non-motor detriments can have the potential to considerably impact subjectively perceived quality of life in patients. Particularly emotion recognition of facial expressions has been shown to be affected in PD, and especially the perception of negative emotions like fear, anger or disgust is impaired. While emotion processing generally refers to automatic implicit as well as conscious explicit processing, (...) the focus of most previous studies in PD was on explicit recognition of emotions only, while largely ignoring implicit processing deficits. Deep brain stimulation of the subthalamic nucleus (STN-DBS) is widely accepted as therapeutic measure in the treatment of PD and has been shown to advantageously influence motor problems. Among various concomitant non-motor effects of STN-DBS, modulation of facial emotion recognition under subthalamic stimulation has been investigated in previous studies with rather heterogeneous results. While there seems to be consensus regarding the processing of disgust which significantly deteriorates under STN stimulation, findings concerning emotions like fear or happiness report heterogeneous data and seem to depend on various experimental settings and measurements. In the present review, we summarized previous investigations focusing on STN-DBS influence on recognition of facial emotional expressions in patients suffering from PD. In a first step, we provide a synopsis of disturbances and problems in facial emotion processing observed in patients with PD. Secondly, we present findings of STN-DBS influence on facial emotion recognition and especially highlight different impacts of stimulation on implicit and explicit emotional processing. (shrink)

Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of (...) its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search forgenerative model-based andgeometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs intoexplicitandimplicitmethods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs. (shrink)