EEG Emotion Recognition
8 papers with code • 2 benchmarks • 3 datasets
Emotion Recognition using EEG signals
SEED
DEAP
PhyMER
Most implemented papers
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.
FOIT: Fast Online Instance Transfer for Improved EEG Emotion Recognition
However, most of them are iterative methods, which need considerable training time and are unfeasible in practice.
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
yi-ding-cs/LGG
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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
VoiceBeer/MS-MDA
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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.
EEG-Based Emotion Recognition Using Genetic Algorithm Optimized Multi-Layer Perceptron
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
meiyor/deep-learning-emotion-decoding-using-eeg-data-from-autism-individuals
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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: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
chen-xdu/gmss
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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.
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
A natural method is to learn the temporal dynamic patterns.
