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Found 9 papers, 2 papers with code
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.
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.
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition
We perform extensive experiments on three public multimodal wearable datasets, WESAD, SWELL-KW, and CASE, and demonstrate that our method can effectively regulate and share information between different modalities to learn better representations.
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.
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.
Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data
The results show the wide-spread applicability for stacked convolutional autoencoders to be used with machine learning for affective computing.
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.
