most citedEnhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis

8 citations · 10 across the 4 of their papers we have counts for

collaborators

6 papers

cs.RO2026

Mamba-based Selective State Space Modeling Improves the Accuracy-Complexity Tradeoff of SmolVLA Vision-Language-Action Experts

Farida Mohsen, Thowayba Elkaffash, Mohammad Reza Chalak Qazani +3

Vision-language-action (VLA) models face a crucial tradeoff between their task success rate and the policy-call frequency. Executing a single action per inference () enables a…

cs.LG20268 cited

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…

cs.HC20261 cited

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…

cs.HC20261 cited

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…

cs.LG2026

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…

cs.RO2025

Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing

Camilo Gonzalez Arango, Houshyar Asadi, Mohammad Reza Chalak Qazani +1

Motion Cueing Algorithms (MCAs) encode the movement of simulated vehicles into movement that can be reproduced with a motion simulator to provide a realistic driving experience wit…