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12 results for “Mental Workload”
Shopping in Immersive Virtual Reality: Effects of Visual, Auditory, and Cognitive Demands on Mental Workload
<p>The dataset - of the journal article "Shopping in Immersive Virtual Reality: Effects of Diminishing Visual, Auditory, and Cognitive Demands on Workload" - consists of heart rate and eye-tracking data per participant and experimental condition. It also contains the figures inserted in the manuscript, the MATLAB scripts, the Unity project of an immersive virtual supermarket, and a demo video of a participant performing a grocery task across the experimental conditions in the virtual supermarket.</p>
An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021
<p>The dataset is part of a new open EEG database designed to answer a need for more publicly available EEG-based dataset to design and benchmark passive brain-computer interface pipelines (as detailed in [Hinss2021]). This database is currently being created and will be fully released before the end of the year. It will include data acquired over 30 participant, 4 tasks and 3 sessions. For this competition, hosted by the Neuroergonomics Conference 2021, only one task and half the participants will be analyzed. Hence, this competition focuses on a renowned task that elicits various levels of mental/cognitive workload: the Multi-Atribute Task Battery-II (MATB-II) developed by NASA (https://matb.larc.nasa.gov/). It is composed of 4 sub-tasks: system monitoring, tracking, resource management and communications. By varying the number and complexity of the sub-tasks, 3 levels of workload were elicited (verified through statistical analyzes of both subjective and objective -behavioral and cardiac- data). Each difficulty level was performed by 15 subjects (6 female; 9 average 25 y.o.) during 5 minutes per session, in a pseudo-randomized order. Each session was separated by 7 days. We used a 62 actiChamp EEG channels device (BrainProducts; electrode placement 10-20 system).</p> <p> </p> <p><strong>For the competition, your goal is to predict the mental workload for a given subject (intra-subject estimation) using the EEG data from another session (inter-session adaptation). More information on the conference website and in the documentation file.</strong></p>
Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning
<p><em>This repository contains all code -from data collection to perceived mental workload classification- used in the "Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning" research and serves as supplementary material. <strong>A clear README file is provided, please refer to that for information about the individual scripts and their usage. </strong></em></p> <p>Mental workload detection has been attempted using various bio-signals. Recently, deep learning has allowed for novel methods and results within the BCI community. However, studies currently often only use a single modality to classify mental workload, whereas a plethora of modalities have proven to be valuable in this task.</p> <p>A dataset on which these scripts was also made publicly available under the following DOI: 10.4121/12932801<br> This dataset contains data collected during research into mental workload (MWL) detection using deep learning. It is being made public as supplementary data for publications, as well as for reuse in research that seeks to classify MWL using multimodal physiological data. The goal of this repository and dataset is to serve as a testing ground for the creation of deep neural networks that can classify MWL using multimodal physiological data.</p> <p>The data in this dataset was collected in the Behavioural, Management, and Social Sciences Lab, University of Twente, Enschede, The Netherlands in June/July 2020.</p>
UNIVERSE: UNobtrusIVE measuRement of mental workload and stress in uncontrolled environments
<p><strong>A Dataset on Unobtrusive Measurement of Cognitive Load and Physiological Signals (EEG, PPG, EDA) in Uncontrolled Environments</strong></p> <p>The dataset (approximately 315 hours in total) consists of physiological signals from wearable electroencephalography (EEG), electrodermal activity (EDA), photoplethysmogram (PPG), acceleration, and temperature sensors. The recorded dataset is curated from 24 participants following an eight-hour cognitive load elicitation paradigm. The mentioned consumer-grade physiological signals are obtained from the Muse S EEG headband and Empatica E4 wristband. The data is balanced across controlled and uncontrolled environments and high vs. low mental workload levels. During the study, participants worked on mental arithmetic, Stroop, N-Back, and Sudoku tasks in the controlled environment (roughly half of the data) and realistic home-office tasks such as researching, programming, and writing emails in uncontrolled environments. Data labels were obtained using Likert scales, Affective Sliders, PANAS, and NASA-TLX questionnaires. The completely anonymized data set and its publicly available features open a vast potential to the research community working on mental workload detection using consumer-grade wearable sensors. Among others, the data is suitable for developing real-time cognitive load detection methods, research on signal processing techniques for challenging environments, developing artifact removal techniques from low-cost wearable devices' data, or developing personal mental workload assistants.<br> </p> <p>Follow the dataset descriptor publication for more details:</p> <p>Anders, C., Moontaha, S., Real, S., Arnrich, B.: Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments. Scientific Data 11(1), 1000 (2024).</p> <p><a href="https://doi.org/https://doi.org/10.1038/s41597-024-03738-7" target="_blank" rel="noopener">https://doi.org/https://doi.org/10.1038/s41597-024-03738-7</a></p>
Characterization of Independant Task Neural Correlates of Different Levels of Mental Workload
ClinicalTrials.gov study NCT02843919. IPD Sharing: Not stated. Countries: 1. Publications: 29.
Impact of Anesthesia Alarm Volume on Mental Workload in Surgical Trainees
ClinicalTrials.gov study NCT07089485. IPD Sharing: NO. Countries: 0. Publications: 10.
Influence of Type 1 Diabetes (T1DM) Treatment Type on Mental Workload
ClinicalTrials.gov study NCT06165159. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effect of ScopeGuide on the Mental Workload of Endoscopist
ClinicalTrials.gov study NCT02092493. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of the Prosthetic System Impact on Mental Workload in Above-knee Lower Limb Amputees.
ClinicalTrials.gov study NCT06635655. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Combined Effects of Acute Sleep Restriction and Moderate Acceleration (+Gz) on Physiological and Behavioral Responses to High Mental Workload
ClinicalTrials.gov study NCT06017882. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Effects of Breakfast on Mental Workload
ClinicalTrials.gov study NCT01555385. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Modeling Variation of the Objective Mental Workload for Tasks Requiring Different Cognitive Functions.
ClinicalTrials.gov study NCT05185102. IPD Sharing: NO. Countries: 1. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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