Privacy-Preserving Brain–Computer Interfaces: A Systematic Review

Xia, Kun; Duch, Włodzisław; Sun, Yu; Xu, Kedi; Fang, Weili; Luo, Hanbin; Zhang, Yi; Sang, Dong; Xu, Xiaodong; Wang, Fei-Yue; Wu, Dongrui

doi:10.1109/tcss.2022.3184818

Cited by 29 publications

(6 citation statements)

References 93 publications

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“…Secure multi-party computation (SMC) [79] facilitates secure calculations across data distributed among multiple stakeholders or locations, ensuring that collaborations do not compromise data integrity. Additionally, anonymisation and data sanitisation techniques [80] are used to alter data by removing or obscuring personally identifiable information, thus protecting individual identities. Federated learning, as highlighted in Refs.…”

Section: Utilizing Large-scale Eeg: Overcoming Privacy Challenges In ...mentioning

confidence: 99%

Transfer Learning for Non-Invasive BCI EEG Brainwave Decoding

Wei

2024

Transfer Learning - Leveraging the Capability of Pre-Trained Models Across Different Domains [Working Title]

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Brain-computer interfaces (BCIs) represent a rapidly advancing domain that enables the interpretation of human cognitive states and intentions through brainwave analysis. This technology has demonstrated significant potential in augmenting the quality of life for individuals with conditions such as paralysis by decoding their neural patterns. Electroencephalograms (EEG) are the cornerstone of this progress, providing a non-invasive and secure means of capturing brain activity. The integration of machine learning, particularly deep learning techniques, has considerably enhanced the accuracy of EEG interpretation in the last decade. However, a critical challenge persists in the training of machine learning algorithms on EEG data due to pronounced variability among individual brain activities. Such variability can result in suboptimal model performance when data availability is scarce. Transfer learning, a strategy successful in other domains like computer vision and natural language processing, offers a promising avenue to deal with the variability of heterogeneous EEG datasets. This chapter provides a comprehensive review of the current state of EEG transfer learning methodologies and an outlook on large-scale brainwave decoding.

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Section: Utilizing Large-scale Eeg: Overcoming Privacy Challenges In ...mentioning

confidence: 99%

Transfer Learning for Non-Invasive BCI EEG Brainwave Decoding

Wei

2024

Transfer Learning - Leveraging the Capability of Pre-Trained Models Across Different Domains [Working Title]

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“…The use of BCIs raises important issues regarding the privacy and security of their users. In the scientific literature, there are several papers, with an increasing trend over the last years, showing that brain data could be extracted through malicious external action or there could be a case of unauthorized access to the functions of BCIs with further impact on the issues of autonomous action and liability [29][30][31][32]. Indeed, the issue of malicious external attacks on the brains of BCI users could be described as a kind of 'neurocrime' [33].…”

Section: Privacy and Securitymentioning

confidence: 99%

Understanding the Ethical Issues of Brain-Computer Interfaces (BCIs): A Blessing or the Beginning of a Dystopian Future?

Livanis,

Voultsos,

Vadikolias

et al. 2024

Cureus

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In recent years, scientific discoveries in the field of neuroscience combined with developments in the field of artificial intelligence have led to the development of a range of neurotechnologies. Advances in neuroimaging systems, neurostimulators, and brain-computer interfaces (BCIs) are leading to new ways of enhancing, controlling, and "reading" the brain. In addition, although BCIs were developed and used primarily in the medical field, they are now increasingly applied in other fields (entertainment, marketing, education, defense industry). We conducted a literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to provide background information about ethical issues related to the use of BCIs. Among the ethical issues that emerged from the thematic data analysis of the reviewed studies included questions revolving around human dignity, personhood and autonomy, user safety, stigma and discrimination, privacy and security, responsibility, research ethics, and social justice (including access to this technology). This paper attempts to address the various aspects of these concerns. A variety of distinct ethical issues were identified, which, for the most part, were in line with the findings of prior research. However, we identified two nuances, which are related to the empirical research on ethical issues related to BCIs and the impact of BCIs on international relationships. The paper also highlights the need for the cooperation of all stakeholders to ensure the ethical development and use of this technology and concludes with several recommendations. The principles of bioethics provide an initial guiding framework, which, however, should be revised in the current artificial intelligence landscape so as to be responsive to challenges posed by the development and use of BCIs.

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“…Furthermore, it has been used to assess cognitive load [4,5,20,24,25], test the effectiveness of different learning strategies [5], evaluate effectiveness in different learning modalities [4,23], and measure cognitive load in both traditional face-to-face learning and online education [20]. In addition, each EEG is individual [26,27] because brain activity can be influenced by many genetic and non-genetic factors, such as stress [15]. However, it has not yet been used in remote learning [2].…”

Section: Introductionmentioning

confidence: 99%

The Use of Integrated Multichannel Records in Learning Studies in Higher Education: A Systematic Review of the Last 10 Years

González-Díez,

Varela,

Sáiz-Manzanares

2024

Computers

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Neurophysiological measures have been used in the field of education to improve our knowledge about the cognitive processes underlying learning. Furthermore, the combined use of different neuropsychological measures has deepened our understanding of these processes. The main objective of this systematic review is to provide a comprehensive picture of the use of integrated multichannel records in higher education. The bibliographic sources for the review were Web of Science, PsycINFO, Scopus, and Psicodoc databases. After a screening process by two independent reviewers, 10 articles were included according to prespecified inclusion criteria. In general, integrated recording of eye tracking and electroencephalograms were the most commonly used metrics, followed by integrated recording of eye tracking and electrodermal activity. Cognitive load was the most widely investigated learning-related cognitive process using integrated multichannel records. To date, most research has focused only on one neurophysiological measure. Furthermore, to our knowledge, no study has systematically investigated the use of integrated multichannel records in higher education. This systematic review provides a comprehensive picture of the current use of integrated multichannel records in higher education. Its findings may help design innovative educational programs, particularly in the online context. The findings provide a basis for future research and decision making regarding the use of integrated multichannel records in higher education.

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Privacy-Preserving Brain–Computer Interfaces: A Systematic Review

Cited by 29 publications

References 93 publications

Transfer Learning for Non-Invasive BCI EEG Brainwave Decoding

Transfer Learning for Non-Invasive BCI EEG Brainwave Decoding

Understanding the Ethical Issues of Brain-Computer Interfaces (BCIs): A Blessing or the Beginning of a Dystopian Future?

The Use of Integrated Multichannel Records in Learning Studies in Higher Education: A Systematic Review of the Last 10 Years

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