Appendix A. Overview of Methods and Results for Emotion Recognition Studies
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Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition
Multi-dimensional labels: valence, arousal, dominance (8-class)
86.43% accuracy
EEG
DREAMER, DEAP, HDED
2024
Feature Selection Methods
Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition
Valence, Arousal, Dominance (binary: high vs. low)
SD: Valence 71.01%, Arousal 78.5%, Dominance 80.92%; SI: Arousal 70.53%, Dominance 75.12%
EEG
DREAMER, DEAP, SEED-IV, MPED
2024
Spiking Neural Network
A Hybrid End-to-End Spatiotemporal Attention Neural Network With Graph-Smooth Signals for EEG Emotion Recognition
Valence, Arousal (binary: high vs. low)
Transfer Learning improved valence to 83% and arousal to 87.2% with 90% DREAMER data
EEG
DEAP, DREAMER, EEWD
2024
Spatiotemporal Attention Network
LUR: An Online Learning Model for EEG Emotion Recognition
Valence (positive/negative), Arousal (high/low)
Arousal: 71%, Valence: 66%; Valence accuracy per subject up to 90%
EEG
DEAP, DREAMER
2023
SVM, Naive Bayes, Online Learning
EEG-Based Emotion Feature Extraction Using Power Spectral Density
Emotion States: Happy (High Valence), Sad (Low Valence)
PSD calculations across theta, alpha, beta bands showed strong correlations for valence (Happy vs. Sad); specific EEG channels showed higher PSD for "Happy" state
EEG
DREAMER
2023
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Robust Emotion Recognition in EEG Signals Based on a Combination of Multiple Domain Adaptation Techniques
Valence: Positivity/Negativity, Arousal: Excitement/Calmness
MDA-NF: Valence 74.5%, Arousal 71.5%; Cross-dataset: DREAMER→DEAP, valence accuracy 64.45%
EEG
DEAP, DREAMER
2023
MDA-NF technique
EEG-Based Emotion Analysis Using Person-Event Network
Valence (Positivity/Negativity), Arousal (Intensity)
Valence accuracy: 83.27%, Arousal accuracy: 88.64%; Better performance with combined person and event-related features
EEG
DEAP, DREAMER
2023
CNN
CycleMVAE: Benchmarking End-to-End Cycle-Consistent Multi-Task Variational Autoencoder for EEG-Based Emotion Recognition
Arousal, Valence, Dominance (High/Low)
F1-Score: Arousal 0.69, Valence 0.68, Dominance 0.71; Accuracy: 78.21% - 80.78%
EEG
DREAMER
2023
VAE
A Hierarchical Three-Dimensional MLP-Based Model for EEG Emotion Recognition
Valence (Positivity/Negativity), Arousal (Excitement/Calmness)
Accuracy: Valence 62.51%, Arousal 64.49%; Hierarchical structure enhances noise robustness
EEG
DEAP, DREAMER, SEED-IV
2023
MLP
EVNCERS: An Integrated Eigenvector Centrality-Variational Nonlinear Chirp Mode Decomposition-Based EEG Rhythm Separation for Automatic Emotion Recognition
Valence, Arousal, Dominance (High/Low); threshold 3 on 1-5 scale
Valence: 62.51%, Arousal: 64.49%; Highlights include using Eigenvector Centrality to enhance feature stability
EEG
DREAMER, SEED
2023
ML classifiers
Emotion Recognition based on fusion of multimodal physiological signals using LSTM and GRU
Valence, Arousal, Dominance (High/Low); threshold 3 on 1-5 scale
Arousal: 96.86% accuracy (F1: 97.98%), Valence: 96.87% accuracy (F1: 97.82%), Dominance: 96.81% accuracy (F1: 97.71%); Delta rhythm shows highest prediction accuracy
EEG, ECG, respiration, temperature
SEED, DREAMER, WESAD
2023
LSTM, GRU, 1DCNN
EEG-Based Emotion Recognition Using Trainable Adjacency Relation Driven Graph Convolutional Network
Valence, Arousal, Dominance (Sad, Neutral, Happy classes)
SVM/Random Forest accuracy: 50-60%; LSTM: 65%, GRU: 62% –
EEG
DREAMER, DEAP
2023
GCN
Three Dimensional Emotion State Classification based on EEG via Empirical Mode Decomposition
Valence, Arousal, Dominance (High/Low); threshold 3 on 1-5 scale
IMF4 provides best accuracy; Valence: 78.32%, Arousal: 76.22%, Dominance: 77.22%;
EEG
AMIGOS, DREAMER
2023
FFNN
EEG evoked automated emotion recognition using deep convolutional neural network
Valence, Arousal, Dominance (High/Low); 1-3 low, 4-5 high
DCNN Accuracy: Valence 84%, Arousal 94.4%, Dominance 97.6%; Outperforms RF and XGBoost on DREAMER
EEG
DREAMER
2023
DCNN
FedEmo: A Privacy-Preserving Framework for Emotion Recognition using EEG Physiological Data
Valence, Arousal, Dominance (High/Low; threshold >3 high)
Valence: 63.3%, Arousal: 56.7%, Dominance: 52.2%;
EEG
DREAMER
2023
ANN
Bi-CapsNet: A Binary Capsule Network for EEG-Based Emotion Recognition
Valence, Arousal, Dominance (Low ≤3, High >3)
Bi-CapsNet showed <1% accuracy drop vs. FP-CapsNet; achieved high accuracy, lower computation costs, and 5x speed on mobile vs. FP-CapsNet
EEG
DEAP, DREAMER
2023
Binary capsule network
A Novel Tensorial Scheme for EEG-Based Person Identification
Person Identification
Study on person identification accuracy via EEG features on DREAMER and DEAP datasets
EEG
DEAP, SEED, DREAMER
2023
-
SSTD: A Novel Spatio-Temporal Demographic Network for EEG-Based Emotion Recognition
Valence, Arousal (Binary; median 3)
Valence: 76.81%, Arousal: 81.64%;
EEG
DEAP, DREAMER
2023
GRU, SPDNet
Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition
Arousal, Valence, Dominance (Binary; Low/High)
Arousal: 91.55%, Dominance: 89.37%, Valence: 87.43%;
EEG
SEED, Dreamer
2023
Residual learning blocks, Graph convolutional broad network
EEG-Based Emotion Recognition via Neural Architecture Search
Arousal, Valence, Dominance (Binary; High/Low with threshold of 3)
Arousal: 96.62%, Valence: 96.29%, Dominance: 96.61%
EEG
DEAP, DREAMER
2023
Neural architecture search, Reinforcement learning
AT2GRU: A Human Emotion Recognition Model With Mitigated Device Heterogeneity
Valence, Arousal, Dominance (Multi-class; 1-5 scale)
Valence: 84.15%, Arousal: 84.69%, Dominance: 85.81%;
EEG, ECG
DREAMER, DEAP
2023
Wavelet filters, GRU
Variational Instance-Adaptive Graph for EEG Emotion Recognition
Valence, Arousal (Binary; Low/High)
Valence: 92.82%, Arousal: 93.09% using subject-dependent evaluation
EEG
SEED, MPED, DREAMER
2023
Multi-level and multi-graph convolution operation
SparseDGCNN: Recognizing Emotion From Multichannel EEG Signals
Valence, Arousal (Binary; Positive/Negative, High/Low)
Subject-Dependent: Avg. 12.66% accuracy and 12.45% F1 improvement; Subject-Independent: Avg. 4.69% accuracy and 8.51% F1 improvement
EEG
SEED, DEAP, DREAMER, CMEED
2023
Forward-backward splitting method
EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention
Valence, Arousal, Dominance (Binary; High/Low)
Valence: 97.93%, Arousal: 97.98%, Dominance: 98.23% with ACRNN model
EEG
DEAP, DREAMER
2023
Channel-wise attention, Self-attention
Time-Varying Graph Signal Processing Based Cross-Subject Emotion Classification from Multi-Electrode EEG Signals
Valence, Arousal (Binary; Positive/Negative, High/Low with threshold 3)
Valence: 97.83%, Arousal: 93.56%; 10-fold cross-validation used
EEG
DREAMER
2022
KNN classifier
EEG-based Emotion Identification using General Factor Analysis
Valence, Arousal, Dominance (Binary; Low <3, High ≥3)
Valence: 96.8%, Arousal: 94.2%, Dominance: 92.6% with SVM
EEG
DREAMER
2022
SVM, KNN
Exploiting Multiple EEG Data Domains with Adversarial Learning
Discrete Emotion States (Negative, Neutral, Positive)
Multi-label classification: 40.48% accuracy; Binary classification (Pos/Neg): 58.17% accuracy
EEG
SEED, SEED-IV, DEAP, DREAMER
2022
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Joint Temporal Convolutional Networks and Adversarial Discriminative Domain Adaptation for EEG-Based Cross-Subject Emotion Recognition
Arousal, Valence (Binary; High/Low via k-means)
Valence: 66.56%, Arousal: 63.69%;
EEG
DEAP, DREAMER
2022
Temporal Convolutional Networks (TCN), Adversarial Discriminative Domain Adaptation (ADDA)
Ensemble Machine Learning-Based Affective Computing for Emotion Recognition Using Dual-Decomposed EEG Signals
Arousal, Valence (Multiclass)
Arousal: 95.81%, Valence: 95.53%;
EEG
DREAMER, SEED, INTERFACES, MUSEC
2022
Ensemble Machine Learning Algorithms
Graph-Embedded Convolutional Neural Network for Image-Based EEG Emotion Recognition
Valence and Arousal (Binary; High/Low)
Valence: 95.73%, Arousal: 92.79%
EEG
SEED, SDEA, DREAMER, MPED
2022
GECNN with attention mechanism and dynamical graph filtering
Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models
Arousal, Valence, Dominance (Binary; High/Low)
Arousal: 89%, Valence: 90.6%, Dominance: 90.7%
EEG, Eye Movement
SEED, SEED-IV, DEAP, SEED-V, DREAMER
2022
DCCA with weighted sum and attention-based fusion
Advancing Remote Healthcare Using Humanoid and Affective Systems
Valence, Arousal (Binary; High >3, Low ≤3)
Capsule Network Accuracy: 90.4%
ECG, GSR
DREAMER, AMIGOS
2022
Deep Learning Algorithms
GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition
Valence, Arousal, Dominance (Binary; High >2, Low ≤2)
Valence: 86.99%, Arousal: 89.32%, Dominance: 89.20%
EEG
SEED, DREAMER
2022
Graph Convolutional Broad Network (GCB-Net)
Utilizing Deep Learning Towards Multi-Modal Bio-Sensing and Vision-Based Affective Computing
Valence, Arousal (Binary; High/Low)
Valence: 79.95%, Arousal: 79.95%, Emotion Classification: 55.56%
EEG, ECG, GSR, PPG
DEAP, MAHNOB-HCI, AMIGOS, DREAMER
2022
Deep Learning Algorithms
Human Emotion Recognition using EEG Signal in Music Listening
Discrete emotion states (9 classes)
k-NN: 94.49%, RF: 99.94%, XGBoost: 99.39%, CNN: 99.91%
EEG
DREAMER
2021
k-NN, Random Forest, XGBoost, CNN
Evaluation of TEAP and AuBT as ECG's Feature Extraction Toolbox
Valence, Arousal (Binary; High/Low)
TEAP (Arousal): 65.4%, AuBT (Valence): 65.8%
ECG
DREAMER, AuBT dataset
2021
Support Vector Machine (SVM)
Feature Reconstruction Based Channel Selection for Emotion Recognition Using EEG
Valence (Binary; Positive/Negative)
Baseline AUC with 14 channels: 0.83, AUC with 8 channels: 0.80
EEG
AMIGOS, DREAMER, DEAP
2021
Machine Learning Models
Online Cross-subject Emotion Recognition from ECG via Unsupervised Domain Adaptation
Valence, Arousal (Binary; Positive/Negative, High/Low)
Valence Accuracy: 72% (F1: 0.69), Arousal Accuracy: 71% (F1: 0.65)
ECG
Dreamer, Amigos
2021
Unsupervised Domain Adaptation
A Computerized Approach for Automatic Human Emotion Recognition Using Sliding Mode Singular Spectrum Analysis
Arousal, Dominance, Valence
(Binary; High/Low)
KNN: Arousal 92.06%, Dominance 92.38%, Valence 92.3%; SVM: Arousal 92.01%, Dominance 92.29%, Valence 91.53%
EEG, ECG
DREAMER, AMIGOS
2021
Machine Learning Classifiers
A Novel Spatio-Temporal Field for Emotion Recognition Based on EEG Signals
Valence, Arousal
(Binary: High vs. Low)
Valence: 81.2%, Arousal: 82.4%
EEG
DEAP, DREAMER
2021
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eRAD-Fe: Emotion Recognition-Assisted Deep Learning Framework
Valence, Arousal (Four Classes: HVHA, LVHA, HVLA, LVLA)
LSTM Accuracy: 81.51%; ET-MCSP feature extraction, 10-sec window with 30% overlap
EEG
SEED, DEAP, DREAMER
2021
Long Short-Term Memory (LSTM)
Attention-based Spatio-Temporal Graphic LSTM for EEG Emotion Recognition
Valence, Arousal
(Binary Classification)
EEG
DEAP, DREAMER
2021
Long Short-term Memory (LSTM), Graph Convolution
EEG Emotion Recognition Based on 3-D Feature Representation and Dilated Fully Convolutional Networks
Valence, Arousal, Dominance
(Binary: Low vs. High)
Subject-dependent: Valence 76.88%, Arousal 75.92%, Dominance 77.43%; LOSO cross-validation
EEG
DEAP, DREAMER
2021
Convolutional Neural Networks
FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition
Valence, Arousal, Dominance (Binary)
Valence: 89.91%, Arousal: 87.67%, Dominance: 90.28%
EEG
DEAP, DREAMER
2021
Neural Network
A Novel Multivariate-Multiscale Approach for Computing EEG Spectral and Temporal Complexity for Human Emotion Recognition
Valence, Arousal, Dominance
(Binary: High vs. Low)
Valence: 86.2%, Arousal: 84.5%, Dominance: 83.9%
EEG
SJTU emotion EEG dataset (SEED), DREAMER emotion EEG public database
2021
Sparse autoencoder, Random forest
Emotion Recognition From Multi-Channel EEG via Deep Forest
Valence, Arousal, Dominance
(Binary: High >3, Low ≤3)
Valence: 89.03%, Arousal: 90.41%, Dominance: 89.89%
Multi-channel EEG
DEAP, DREAMER
2021
Deep forest
BioCNN: A Hardware Inference Engine for EEG-Based Emotion Detection
Valence, Arousal, Dominance
(Binary)
Valence: 89.03%, Arousal: 90.41%, Dominance: 89.89%
EEG
DEAP, DREAMER, In-house dataset
2020
Convolutional Neural Network
EEG feature learning with Intrinsic Plasticity based Deep Echo State Network
Valence, Arousal, Dominance
(Binary: High ≥3, Low <3)
Valence: 82.11%, Arousal: 83.58%, Dominance: 84.98%
EEG
DEAP, DREAMER
2020
Deep echo state network, Intrinsic plasticity, Ridge regression, Online delta rule
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
Valence, Arousal, Dominance
(Binary: High ≥3, Low <3)
Valence: 86.23% (SD 12.29%), Arousal: 84.54% (SD 10.18%), Dominance: 85.02% (SD 10.25%)
Multi-channel EEG
SEED, DREAMER
2020
DGCNN
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