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Found 5 papers, 1 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.
A Transformer Architecture for Stress Detection from ECG
Electrocardiogram (ECG) has been widely used for emotion recognition.
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition
Classification of human emotions can play an essential role in the design and improvement of human-machine systems.
Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning
Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills.
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