Objective: Brain-computer interface (BCI) research and commercially available neural devices generate large amounts of neural data. These data have significant potential value to researchers and industry. Individuals from whose brains neural data derive may want to exert control over what happens to their neural data at study conclusion or as a result of using a consumer device. It is unclear how BCI researchers understand the relationship between neural data and BCI users and what control individuals should have over their neural data.
Approach: An online survey of BCI researchers (n = 122) gathered perspectives on control of neural data generated in research and non-research contexts. The survey outcomes are discussed and other relevant concerns are highlighted.
Main Results: The study found that 58% of BCI researchers endorsed giving research participants access to their raw neural data at the conclusion of a study. However, researchers felt that individuals should be limited in their freedom to either donate or sell these data. A majority of researchers viewed raw neural data as a kind of medical data. Survey respondents felt that current laws and regulations were inadequate to protect consumer neural data privacy, though many respondents were also unfamiliar with the details of existing guidelines.
Significance: The majority of BCI researchers believe that individuals should have some but not unlimited control over neural data produced in research and non-research contexts.
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http://dx.doi.org/10.1088/1741-2552/ab5b7f | DOI Listing |
J Neural Eng
January 2025
Department of Neuroscience, Northwestern University, 303 East Chicago Ave, Chicago, Illinois, 60611, UNITED STATES.
Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.
View Article and Find Full Text PDFPLoS One
January 2025
School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
With the popularity of circular economy around the world, transactions in the second-hand sailboat market are extremely active. Determining pricing strategies and exploring their regional effects is a blank area of existing research and has important practical and statistical significance. Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data.
View Article and Find Full Text PDFIn the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and networks. Generally, attackers perform it to make reprisal but sometimes this issue can be authentic also.
View Article and Find Full Text PDFPLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130.
Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions.
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