205 citations · 464 across the 19 of their papers we have counts for
7 papers · 1 filter
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
Guangyi Zhang, Vandad Davoodnia, Ali Etemad
We propose PARSE, a novel semi-supervised architecture for learning strong EEG representations for emotion recognition. To reduce the potential distribution mismatch between the la…
Holistic Semi-Supervised Approaches for EEG Representation Learning
Guangyi Zhang, Ali Etemad
Recently, supervised methods, which often require substantial amounts of class labels, have achieved promising results for EEG representation learning. However, labeling EEG data i…
Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition
Guangyi Zhang, Ali Etemad
EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often expensive…
Distilling EEG Representations via Capsules for Affective Computing
Guangyi Zhang, Ali Etemad
Affective computing with Electroencephalogram (EEG) is a challenging task that requires cumbersome models to effectively learn the information contained in large-scale EEG signals,…
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation
Guangyi Zhang, Ali Etemad
Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring…
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network
Guangyi Zhang, Vandad Davoodnia, Alireza Sepas-Moghaddam +2
Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging fro…
Errors-in-variables Modeling of Personalized Treatment-Response Trajectories
Guangyi Zhang, Reza Ashrafi, Anne Juuti +2
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a co…