The dataset contains the following three measures that are widely used to determine cognitive load in humans: Detection Response Task - response time, pupil diameter, and eye gaze. These measures were recorded from 28 participants while they underwent tasks that are designed to permeate three different cognitive difficulty levels. The dataset will be useful to those researchers who seek to employ low cost, non-invasive sensors to detect cognitive load in humans and to develop algorithms for human-system automation. One such application is found in Advanced Driver Assistance Systems where eye-trackers are employed to monitor the alertness of the drivers. The dataset would also be helpful to researchers who are interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine system automation. The data is collected by the authors at the Department of Electrical & Computer Engineering in collaboration with the Faculty of Human Kinetics at the University of Windsor under the guidance of their Research Ethics Board.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578673 | PMC |
| http://dx.doi.org/10.1016/j.dib.2020.106389 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Daily Cognition in the Family Context: The Complex Associations between Memory Lapses, Family Relationships, and Affect in Middle-Aged and Older Adults.
Gerontologist
January 2025
Department of Psychology, Clemson University, Clemson, South Carolina, USA.
Background And Objectives: The analysis of daily memory lapses is an underutilized approach to understanding daily experiences of cognitive functioning. The present study adopts this approach, with the goals of exploring how the quality of family relationships predicts the frequency of daily memory lapses and moderates the link between daily memory lapses and daily affect.
Research Design And Methods: We used longitudinal data from the third wave of Midlife in the United States and the National Study of Daily Experiences to assess our research goals.
Targeting accuracy of neuronavigation: a comparative evaluation of an innovative wearable AR platform vs. traditional EM navigation.
Front Digit Health
January 2025
Department of Information Engineering, University of Pisa, Pisa, Italy.
Wearable augmented reality in neurosurgery offers significant advantages by enabling the visualization of navigation information directly on the patient, seamlessly integrating virtual data with the real surgical field. This ergonomic approach can facilitate a more intuitive understanding of spatial relationships and guidance cues, potentially reducing cognitive load and enhancing the accuracy of surgical gestures by aligning critical information with the actual anatomy in real-time. This study evaluates the benefits of a novel AR platform, VOSTARS, by comparing its targeting accuracy to that of the gold-standard electromagnetic (EM) navigation system, Medtronic StealthStation S7.
View Article and Find Full Text PDFDifferent Imaging Evaluating Performances Between Glymphatic System and Motor Symptoms and Levodopa Responsiveness of Parkinson Disease.
J Comput Assist Tomogr
January 2025
Department of Radiology, the First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen.
Background And Purpose: Parkinson disease (PD) is defined by its unique motor symptoms, where responsiveness to levodopa (L-DOPA) is fundamental for management. Recent research has highlighted a significant relationship between PD symptoms and glymphatic dysfunction. This study endeavors to clarify the connection between glymphatic system functionality and initial motor symptoms in PD, utilizing imaging biomarkers to determine its predictive capacity for L-DOPA responsiveness (LR).
View Article and Find Full Text PDFDeep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.
Clin Nucl Med
January 2025
Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Purpose: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18F-FBB PET/CT without relying on high-resolution MRI.
Patients And Methods: A retrospective dataset of PET/CT and T1-weighted MRI pairs from 226 individuals (157 with mild cognitive impairment or dementia and 69 healthy controls) was used. The dataset was split into training/validation (60%) and test (40%) sets.
What makes a trauma 'pathological'? - Perceived peritraumatic threat influences the development of intrusive memories.
Behav Res Ther
January 2025
Department of Psychology, University of Greifswald, Franz-Mehring-Straße 47, 17489, Greifswald, Germany; Department of Psychology, University of Hildesheim, Universitätsplatz 1, 31141, Hildesheim, Germany.
Intrusions are a hallmark symptom of posttraumatic stress disorder (PTSD). While dysfunctional cognitions are known posttraumatic contributors, peritraumatic processes are less understood. Perceived threat, alongside emotional factors, is theorized as significant, but experimental studies are lacking.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!