Search Results for author:
Found 4 papers, 2 papers with code
CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
In contrast, for LOSO, the best performance is achieved by the deep learning model with ECG, EDA, and EEG.
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) as well as eye tracking data.
Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning
The eye region isolation is performed with a U-Net style network which we train using a synthetic dataset that contains eye region masks for the visible eyeball and the iris region.
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning
We first create a synthetic dataset containing eye region masks detailing the visible eyeball and iris using a simulator.
