Functional near-infrared spectroscopy (fNIRS) and Eye tracking for Cognitive Load classification in a Driving Simulator Using Deep Learning

@inproceedings{Khan2024,
title = {Functional near-infrared spectroscopy (fNIRS) and Eye tracking for Cognitive Load classification in a Driving Simulator Using Deep Learning},
author = {Mehshan Ahmed Khan and Houshyar Asadi and Mohammad Reza Chalak Qazani and Chee Peng Lim and Saied Nahavandi},
editor = {Andras Kemeny and Jean-Rémy Chardonnet and Florent Colombet and Stéphane Espié},
isbn = {978-2-9573777-5-6},
year  = {2024},
date = {2024-09-18},
booktitle = {Proceedings of the Driving Simulation Conference 2024 Europe VR},
pages = {47-56},
address = {Strasbourg, France},
organization = {Driving Simulation Association},
abstract = {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 only employ two levels of workload, leaving a gap in research on employing deep learning methodologies to analyze cognitive load, especially in challenging low-light conditions. Often, studies overlook or solely focus on scenarios in bright daylight. To address this gap and understand the correlation between performance and cognitive load, this study employs functional near-infrared spectroscopy (fNIRS) and eye-tracking data, including fixation duration and gaze direction, during simulated driving tasks in low visibility conditions, inducing various mental workloads. The first stage involves the statistical estimation of useful features from fNIRS and eye-tracking data. ANOVA will be applied to the signals to identify significant channels from fNIRS signals. Optimal features from fNIRS, eye-tracking and vehicle dynamics are then combined in one chunk as input to the CNN and LSTM model to predict workload variations. The proposed CNN-LSTM model achieved 99% accuracy with neurological data and 89% with vehicle dynamics to predict cognitive load, indicating potential for real-time assessment of driver mental state and guide designers for the development of safe adaptive systems.},
keywords = {},
}

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TY  - CONF
TI  - Functional near-infrared spectroscopy (fNIRS) and Eye tracking for Cognitive Load classification in a Driving Simulator Using Deep Learning
AU  - Khan, Mehshan Ahmed
AU  - Asadi, Houshyar
AU  - Qazani, Mohammad Reza Chalak
AU  - Lim, Chee Peng
AU  - Nahavandi, Saied
C1  - Strasbourg, France
C3  - Proceedings of the Driving Simulation Conference 2024 Europe VR
DA  - 2024/09/18
PY  - 2024
SP  - 47
EP  - 56
LA  - en-US
PB  - Driving Simulation Association
SN  - 978-2-9573777-5-6
L2  - https://proceedings.driving-simulation.org/proceeding/dsc-2024/functional-near-infrared-spectroscopy-fnirs-and-eye-tracking-for-cognitive-load-classification-in-a-driving-simulator-using-deep-learning
ER  - 

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