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A Review on Privacy and Monetization Aspects Within BCI and XR-BCI Ecosystems
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Tech giants like Apple, Google, and Amazon are increasingly developing apps and services offering mental health treatment. The value of these products for users is dubious — but they do promise the companies lucrative new sources of highly personal data.
Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superior temporal gyrus (STG), evidenced a new STG subregion tuned to musical rhythm, and defined an anterior–posterior STG organization exhibiting sustained and onset responses to musical elements. Our findings show the feasibility of applying predictive modeling on short datasets acquired in single patients, paving the way for adding musical elements to brain–computer interface (BCI) applications.
A brain–computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain–computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain–computer interfaces.
Metaverse is a living space and cyberspace that realizes the process of virtualizing and digitizing the real world. It integrates a plethora of existing technologies with the goal of being able to map the real world, even beyond the real world. Metaverse has a bright future and is expected to have many applications in various scenarios. The support of the Metaverse is based on numerous related technologies becoming mature. Hence, there is no doubt that the security risks of the development of the Metaverse may be more prominent and more complex. We present some Metaverse-related technologies and some potential security and privacy issues in the Metaverse. We present current solutions for Metaverse security and privacy derived from these technologies. In addition, we also raise some unresolved questions about the potential Metaverse. To summarize, this survey provides an in-depth review of the security and privacy issues raised by key technologies in Metaverse applications. We hope that this survey will provide insightful research directions and prospects for the Metaverse's development, particularly in terms of security and privacy protection in the Metaverse.
An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.
Driven by data proliferation, digital technologies have transformed the marketing landscape. In parallel, significant privacy concerns have shaken consumer-firm relationships, prompting changes in both regulatory interventions and people's own privacy-protective behaviors. With a comprehensive analysis of digital technologies and data strategy informed by structuration theory and privacy literature, the authors consider privacy tensions as the product of firm-consumer interactions, facilitated by digital technologies. This perspective in turn implies distinct consumer, regulatory, and firm responses related to data protection. By consolidating various perspectives, the authors propose three tenets and seven propositions, supported by interview insights from senior managers and consumer informants, that create a foundation for understanding the digital technology implications for firm performance in contexts marked by growing privacy worries and legal ramifications. On the basis of this conceptual framework, they also propose a data strategy typology across two main strategic functions of digital technologies: data mone-tization and data sharing. The result is four distinct types of firms, which engage in disparate behaviors in the broader ecosystem pertaining to privacy issues. This article also provides directions for research, according to a synthesis of findings from both academic and practical perspectives.
Digital personal data is increasingly framed as the basis of contemporary economies, representing an important new asset class. Control over these data assets seems to explain the emergence and dominance of so-called "Big Tech" firms, consisting of Apple, Microsoft, Amazon, Google/Alphabet, and Facebook. These US-based firms are some of the largest in the world by market capitalization, a position that they retain despite growing policy and public condemnation-or "techlash"-of their market power based on their monopolistic control of personal data. We analyse the transformation of personal data into an asset in order to explore how personal data is accounted for, governed, and valued by Big Tech firms and other political-economic actors (e.g., investors). However, our findings show that Big Tech firms turn "users" and "user engagement" into assets through the performative measurement, governance, and valuation of user metrics (e.g., user numbers, user engagement), rather than extending ownership and control rights over personal data per se. We conceptualize this strategy as a form of "techcraft" to center attention on the means and mechanisms that Big Tech firms deploy to make users and user data measurable and legible as future revenue streams.
This document aims to conduct a literature review in order to identify evolution and research trends in the area of neuromarketing. To achieve this objective, a science mapping approach was adopted. Science mapping is an innovative and appropriate tool used in systematic literature reviews by integrating bibliometrics and network analyses. In the case of this paper, an exploration of the Web of Science (WoS) and Scopus databases was carried out. The records obtained enabled the construction of the network of the most relevant documents in the field, which were categorized into “classical”, “structural”, and “recent”. This process allowed for the identification of three perspectives or research correlates. Additionally, it became evident that neuromarketing is still an immature and incipient area with a low degree of theoretical consensus.
Brain–machine interfaces (BMIs), which enable a two-way flow of signals, information, and directions between human neurons and computerized machines, offer spectacular opportunities for therapeutic and consumer applications, but they also present unique dangers to the safety, privacy, psychological health, and spiritual well-being of their users. The sale of these devices as commodities for profit exacerbates such issues and may subject the user to an unequal exchange with corporations. Catholic healthcare professionals and bioethicists should be especially concerned about the implications for the essential dignity of the persons using the new BMIs.
Summary
The commercial sale of brain-machine interfaces (BMIs) generates and exacerbates problems for end-users' safety, psychological health, and spiritual well-being.
On November 14 last year, the British Guardian published an account from an anonymous whistleblower at Google, accusing the company of misconduct in regard to handling sensitive health data. The whistleblower works for Project Nightingale, an attempt by Google to get into the lucrative US healthcare market, by storing and processing the personal medical data of up to 50 million customers of Ascension, one of America's largest healthcare providers. As the Wall Street Journal had already reported 3 days earlier, and as the whistleblower confirmed, neither was the data anonymized when transmitted from Ascension nor were patients or their doctors notified, let alone asked for consent to sharing their data with Google (Copeland, 2019; Pilkington, 2019). As a result, Google employees had full access to non-anonymous patient health data. Google Health chief David Feinberg commented that all Google employees involved had gone through medical ethics training and were approved by Ascension (Feinberg, 2019).
Value Chain has been considered as a key model for managing efficiently value creation processes within organizations. However, with the digitization of the end-to-end processes which began to adopt data as a main source of value, traditional value chain models have become outdated. For this, researchers have developed new value chain models, called Data Value Chains, to carry out data driven organizations. Thereafter, new data value chains called Big Data Value chain have emerged with the emergence of Big Data in order to face new data-related challenges such as high volume, velocity, and variety. These Big Data Value Chains describe the data flow within organizations which rely on Big Data to extract valuable insights. It is a set of ordered steps using Big Data Analytics tools and mainly built for going from data generation to knowledge creation. The advances in Big Data and Big Data Value Chain, using clear processes for aggregation and exploitation of data, have given rise to what is called data monetization. Data monetization concept consists of using data from an organization to generate profit. It may be selling the data directly for cash, or relying on that data to create value indirectly. It is important to mention that the concept of monetizing data is not as new as it looks, but with the era of Big Data and Big Data Value Chain it is becoming attractive. The aim of this paper is to provide a comprehensive review of value creation, data value, and Big Data value chains with their different steps. This literature has led us to construct an end-to-end exhaustive BDVC that regroup most of the addressed phases. Furthermore, we present a possible evolution of that generic BDVC to support Big Data Monetization. For this, we discuss different approaches that enable data monetization throughout data value chains. Finally, we highlight the need to adopt specific data monetization models to suit big data specificities.
Neuromarketing is an emerging field in which academic and industry research scientists employ neuroscience techniques to study marketing practices and consumer behavior. The use of neuroscience techniques, it is argued, facilitates a more direct understanding of how brain states and other physiological mechanisms are related to consumer behavior and decision making. Herein, we will articulate common ethical concerns with neuromarketing as currently practiced, focusing on the potential risks to consumers and the ethical decisions faced by companies. We argue that the most frequently raised concerns—threats to consumer autonomy, privacy, and control—do not rise to meaningful ethical issues given the current capabilities and implementation of neuromarketing research. But, we identify how potentially serious ethical issues may emerge from neuromarketing research practices in industry, which are largely proprietary and opaque. We identify steps that can mitigate associated ethical risks and thus reduce the threats to consumers. We conclude that neuromarketing has clear potential for positive impact on society and consumers, a fact rarely considered in the discussion on the ethics of neuromarketing.
Neuromarketing is an emerging field that bridges the study of consumer behavior with neuroscience. Controversial when it first
emerged in 2002, the field is gaining rapid credibility and adoption among advertising and marketing professionals. Each year,
over 400 billion dollars is invested in advertising campaigns. Yet, conventional methods for testing and predicting the effectiveness
of those investments have generally failed because they depend on consumers’ willingness and competency to describe how they
feel when they are exposed to an advertisement. Neuromarketing offers cutting edge methods for directly probing minds without
requiring demanding cognitive or conscious participation. This paper discusses the promise of the burgeoning field of neuromarketing
and suggests it has the potential to significantly improve the effectiveness of both commercial and cause-related advertising
messages around the world.
KeywordsNeuromarketing–Advertising–Marketing research–Consumer behavior–
fMRI–EEG–Neuroscience
Facebook, like many communication services and social media sites, uses its Terms of Service (ToS) to forbid children under the age of 13 from creating an account. Such prohibitions are not uncommon in response to the Children’s Online Privacy Protection Act (COPPA), which seeks to empower parents by requiring commercial Web site operators to obtain parental consent before collecting data from children under 13. Given economic costs, social concerns, and technical issues, most general–purpose sites opt to restrict underage access through their ToS. Yet in spite of such restrictions, research suggests that millions of underage users circumvent this rule and sign up for accounts on Facebook. Given strong evidence of parental concern about children’s online activity, this raises questions of whether or not parents understand ToS restrictions for children, how they view children’s practices of circumventing age restrictions, and how they feel about children’s access being regulated. In this paper, we provide survey data that show that many parents know that their underage children are on Facebook in violation of the site’s restrictions and that they are often complicit in helping their children join the site. Our data suggest that, by creating a context in which companies choose to restrict access to children, COPPA inadvertently undermines parents’ ability to make choices and protect their children’s data. Our data have significant implications for policy–makers, particularly in light of ongoing discussions surrounding COPPA and other age–based privacy laws.
The application of neuroimaging methods to product marketing - neuromarketing - has recently gained considerable popularity. We propose that there are two main reasons for this trend. First, the possibility that neuroimaging will become cheaper and faster than other marketing methods; and second, the hope that neuroimaging will provide marketers with information that is not obtainable through conventional marketing methods. Although neuroimaging is unlikely to be cheaper than other tools in the near future, there is growing evidence that it may provide hidden information about the consumer experience. The most promising application of neuroimaging methods to marketing may come before a product is even released - when it is just an idea being developed.
Companies increasingly use virtual reality (VR) for advertising. This begs the question, What risks does VR advertising pose for consumers? We analyze VR marketing experiences to identify risks and discuss opportunities to address those and future risks in VR advertising.
Although researchers have discussed big data for years, they have thus far has scarcely touched on directly selling and monetizing data assets. This aspect has particular relevance given recent concerns about data privacy and security and the simultaneous explosion in the use of data for marketing and service-development purposes. In this paper, we describe an empirical study on companies’ initiatives about selling and monetizing data. We categorize the relevant business models based on several customer-refinement and scalability dimensions. We found several constraints (organization type, business type, data characteristics, privacy, and security) that companies should address to move from internally using data and supporting existing customers to generating new business through selling data. Based on the findings, we propose ways for practitioners to benefit from the data. For researchers, we provide directions for future studies that include developing strategies that foster compliance between companies’ aspirations and consumer/societal restrictions and that facilitate data-based innovation and revenue generation.
A brain-computer interface (BCI) system is a system that leverages brainwave information acquired by a designated brain monitoring device to interact with a computerized system. Over the past 40 years, many BCI applications have been developed in a variety of domains, from entertainment to medical field and even to computer security mechanisms. Until now, the development of BCI systems has focused on improving their accuracy, functionality, and ease of use, and not enough effort and attention has been invested in securing these systems and the sensitive data they acquire. In this article, we present the principles of brain activity data acquisition, with a special focus on EEG, and we present a taxonomy of BCI applications and domains. We also provide a comprehensive survey that covers eight possible attacks aimed at BCI systems. For each BCI application, we created an ecosystem and data and attack flow-diagram, which comprehensively describes the roles and interactions of the players associated with the BCI application and presents the most vulnerable vectors and components within its ecosystem; we identified gaps between existing security solutions and the presented attacks and vulnerabilities. Finally, we provide several concrete suggestions for improving the security of BCI systems in cyber-space.
This paper addresses ‘the biggest lie on the internet’ with an empirical investigation of privacy policy (PP) and terms of service (TOS) policy reading behavior. An experimental survey (N = 543) assessed the extent to which individuals ignored PP and TOS when joining a fictitious social networking service (SNS), NameDrop. Results reveal 74% skipped PP, selecting the ‘quick join’ clickwrap. Average adult reading speed (250–280 words per minute), suggests PP should have taken 29–32 minutes and TOS 15–17 minutes to read. For those that didn’t select the clickwrap, average PP reading time was 73 seconds. All participants were presented the TOS and had an average reading time of 51 seconds. Most participants agreed to the policies, 97% to PP and 93% to TOS, with decliners reading PP 30 seconds longer and TOS 90 seconds longer. A regression analysis identifies information overload as a significant negative predictor of reading TOS upon sign up, when TOS changes, and when PP changes. Qualitative findings suggest that participants view policies as nuisance, ignoring them to pursue the ends of digital production, without being inhibited by the means. Implications are revealed as 98% missed NameDrop TOS ‘gotcha clauses’ about data sharing with the NSA and employers, and about providing a first-born child as payment for SNS access.
An increasing number of Brain-Computer Interfaces (BCIs) are being developed in medical and nonmedical fields, including marketing, gaming and entertainment industries. BCI-enabled technology carries a great potential to improve and enhance the quality of human lives. It provides people suffering from severe neuromuscular disorders with a way to interact with the external environment. It also enables a more personalized user experience in gaming and entertainment. These BCI applications are, however, not without risk. Established engineering practices set guarantees on performance, reliability and physical safety of BCIs. But no guarantees or standards are currently in place regarding user privacy and security. In this paper, we identify privacy and security issues arising from possible misuse or inappropriate use of BCIs. In particular, we explore how current and emerging non-invasive BCI platforms can be used to extract private information, and we suggest an interdisciplinary approach to mitigating this problem. We then propose a tool to prevent this side-channel extraction of users' private information. This is a first step towards making BCI-enabled technologies secure and privacy preserving.
“Retail therapy” is often applied to the notion of trying to cheer oneself up through the purchase of self-treats. The negative moods that lead to retail therapy, however, have also been associated with greater impulsivity and a lack of behavioral control. Does this lead to mindless shopping when consumers are “down” and regret later? The current work documents that a bad mood does lead to greater purchase and consumption of unplanned treats for the self. However, it also provides evidence that the consumption of self-treats can be strategically motivated. Those individuals who do indulge can also exercise restraint if the goal of restraint also leads to improved mood. Finally, retail therapy has lasting positive impacts on mood. Feelings of regret and guilt are not associated with the unplanned purchases made to repair a bad mood. The implications of the research are discussed. © 2011 Wiley Periodicals, Inc.
A spatio-temporal wavelet-chaos methodology is presented for analysis of EEGs and their delta, theta, alpha, and beta sub-bands for discovering potential markers of abnormality in Alzheimer's disease (AD). The non-linear dynamics of the EEG and EEG sub-bands are quantified in the form of the correlation dimension (CD), representing system complexity, and the largest Lyapunov exponent (LLE), representing system chaoticity. The methodology is applied to two groups of EEGs: healthy subjects and AD patients. The eyes open and eyes closed conditions are investigated to evaluate the effect of visual input and attention. EEGs from different loci in the brain are investigated to discover areas of the brain responsible for or affected by changes in CD and LLE. It is found that the wavelet-chaos methodology and the sub-band analysis developed in this research accurately characterizes the non-linear dynamics of non-stationary EEG-like signals with respect to the EEG complexity and chaoticity. It is concluded that changes in the brain dynamics are not spread out equally across the spectrum of the EEG and over the entire brain, but are localized to certain frequency bands and electrode loci. New potential markers of abnormality were discovered in this research for both eyes open and closed conditions.
The mental health app data privacy problem is getting worse
- A Belmonte
Big tech sees neurotechnology as its next ai frontier
- M Mills
Would you pay for a facebook account to protect your privacy?
- S L Baglione
- L A Tucci
- P Woock
Who invited the pay-for-privacy economy?
- C Bryce
Annual revenue generated by meta platforms from
- S J Dixon
Your brain waves are up for sale. a new law wants to change that
- J Moens
Security and privacy for augmented reality: our 10-year retrospective
- F Roesner
- T Kohno
Diagnosis of Alzheimer’s disease from EEG signals: where are we standing?
- J Dauwels
- F Vialatte
- A Cichocki