8 citations · 10 across the 3 of their papers we have counts for
5 papers
A comparative and critical study of EEGNet for fNIRS-driven cognitive load classification
Mehshan Ahmed Khan, Houshyar Asadi, Li Zhang +8
Accurately classifying cognitive load from functional near-infrared spectroscopy (fNIRS) signals remains a significant challenge due to temporal variability, inter-subject differen…
Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model
Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani +3
One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving. Although physiological signals have b…
Functional near-infrared spectroscopy (fNIRS) and Eye tracking for Cognitive Load classification in a Driving Simulator Using Deep Learning
Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani +2
Motion simulators allow researchers to safely investigate the interaction of drivers with a vehicle. However, many studies that use driving simulator data to predict cognitive load…
Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis
Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani +6
Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated h…
A Novel CNN-LSTM-based Approach to Predict Urban Expansion
Wadii Boulila, Hamza Ghandorh, Mehshan Ahmed Khan +2
Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveilling crops, coa…