EEG Emotion Recognition
11 papers with code • 3 benchmarks • 4 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.
Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion Recognition
An objective and accurate emotion diagnostic reference is vital to psychologists, especially when dealing with patients who are difficult to communicate with for pathological reasons.
EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition
yi-ding-cs/emt
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Furthermore, we design a temporal contextual transformer module (TCT) with two types of token mixers to learn the temporal contextual information.
