Appendix A. Overview of Methods and Results for Emotion Recognition Studies
[137]
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
[127]
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
[103]
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
[91]
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
[138]
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
-
[139]
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
[93]
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
[114]
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
[140]
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
[115]
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
[141]
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
[142]
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
[143]
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
[144]
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
[145]
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
[146]
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
[147]
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
-
[148]
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
[149]
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
[150]
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
[151]
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
[117]
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
[94]
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
[152]
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
[89]
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
[90]
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
[153]
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
-
[101]
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)
[154]
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
[155]
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
[156]
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
[157]
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
[158]
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)
[159]
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
[83]
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
[78]
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)
[160]
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
[161]
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
[162]
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
[104]
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
-
[163]
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)
[105]
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
[164]
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
[165]
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
[85]
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
[166]
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
[95]
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
[125]
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
[167]
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
Last updated