EEG Emotion Recognition
8 papers with code • 2 benchmarks • 2 datasets
Emotion Recognition using EEG signals
Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.
However, most of them are iterative methods, which need considerable training time and are unfeasible in practice.
It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers.
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition
Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.
Low/High Valence with an average accuracy of 91. 10% and Low/High Arousal with an average accuracy of 91. 02%, b. four classes of emotions viz.
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism
This study is the first to consolidate a more transparent feature-relevance calculation for a successful EEG-based facial emotion recognition using a within-subject-trained CNN in typically-developed and ASD individuals.
GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks.