Results 1 to 10 of about 3,243,952 (257)

Digital Fatigue and Cognitive Load in E‐Learning: Evidence From Nursing Students in Jordan [PDF]

Nursing Open
Aim To assess digital fatigue and multidimensional cognitive load, and to examine their associated factors among nursing students engaged in online learning in Jordanian universities. Design A cross‐sectional study.
Shaban Sinnokrot, Randa Khirfan, Ahmad I. Miqdadi, Yousef Abu‐Wardeh, Ala'a Ayman Al‐yyan, Qusai Abdulrahman AbuQamar, Nawras Fashafsheh   +6 more
doaj   +2 more sources

Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

IEEE Access, 2022
Dimensionality reduction and the automatic learning of key features from electroencephalographic (EEG) signals have always been challenging tasks. Variational autoencoders (VAEs) have been used for EEG data generation and augmentation, denoising, and ...
Taufique Ahmed, Luca Longo
doaj   +1 more source

Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations

Machine Learning and Knowledge Extraction, 2023
Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare.
Robert S. Sullivan, Luca Longo
doaj   +1 more source

Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility

Information, 2023
Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. Variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction.
Taufique Ahmed, Luca Longo
doaj   +1 more source

On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

Machine Learning and Knowledge Extraction, 2022
Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis.
Arjun Vinayak Chikkankod, Luca Longo
doaj   +1 more source

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning

Brain Sciences, 2022
The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to
Luca Longo
doaj   +1 more source

Human Mental Workload: A Survey and a Novel Inclusive Definition

Frontiers in Psychology, 2022
Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics.
Luca Longo, Christopher D. Wickens, Gabriella Hancock, P. A. Hancock   +3 more
doaj   +1 more source

Measurement of Extraneous and Germane Cognitive Load in the Mathematics Addition Task: An Event-Related Potential Study

Brain Sciences, 2022
Cognitive load significantly influences learning effectiveness. All the three types of cognitive load—intrinsic, extraneous, and germane—are important for guiding teachers in preparing effective instructional designs for students. However, the techniques
Chao-Chih Wang, Peter Kuan-Hao Cheng, Tzu-Hua Wang   +2 more
doaj   +1 more source

Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human
Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo   +3 more
doaj   +1 more source

Confirmatory Factor Analysis of Cognitive Load Ratings Supports a Two-Factor Model [PDF]

Tutorials in Quantitative Methods for Psychology, 2020
The practically usable measures of cognitive load are important for evaluating learning and performance conditions, and obtaining empirical evidence in support of theoretical hypotheses.
Jiang, Dayu, Kalyuga, Slava
doaj   +1 more source