Zunayed Mahmud
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View article: CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
CLARE: Cognitive Load Assessment in REaltime with Multimodal Data Open
We present a novel multimodal dataset for Cognitive Load Assessment in REal-time (CLARE). The dataset contains physiological and gaze data from 24 participants with self-reported cognitive load scores as ground-truth labels. The dataset co…
View article: Multimodal Brain–Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
Multimodal Brain–Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines Open
CL-Drive is a driver cognitive load assessment dataset that contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA), and eye tracking data. We ha…
View article: Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning
Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning Open
We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolati…
View article: Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning Open
We present a novel multistream network that learns robust eye representations for gaze estimation. We first create a synthetic dataset containing eye region masks detailing the visible eyeball and iris using a simulator. We then perform ey…
View article: Electric vehicles (EVs): a study on its many principles, interior functioning and modeling through software simulation
Electric vehicles (EVs): a study on its many principles, interior functioning and modeling through software simulation Open
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2018.