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Found 3 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.
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning
We pre-train our model using self-supervised masked autoencoding on emotion-related EEG datasets and use transfer learning with both frozen weights and fine-tuning to perform downstream cognitive load classification.
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.
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