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- | **Article** | **Year** | **Github Link/ Repo** | **DA/DG** | **Method** | **Dataset** | **Architecture** | **Multi-source or Single-source** | **Task** | **Domain** |
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- | EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network | 2022 | [link](https://github.com/cuijiancorbin/EEG-based-CrossSubject-Driver-Drowsiness-Recognition-with-anInterpretable-CNN) | DG | Architecture Embedded | Others | CNN | SS | Drowsiness Recognition | Cross-Subject |
- | Domain-Invariant Representation Learning from EEG with Private Encoders | 2022 | - | DG | Multi-source Domain Alignment | SEED - DEAP - SEED-IV - DREAMER | ANN | MS | Emotion Recognition | Cross-Subject |
- | Capsule neural networks on spatio-temporal EEG frames for cross-subject emotion recognition | 2022 | - | DG | Architecture Embedded | DEAP | CNN | SS | Drowsiness Recognition | Cross-Subject |
- | Privacy-Preserving Domain Adaptation for Motor Imagery-based Brain-Computer Interfaces | 2022 | [link](https://github.com/xkazm/ASFA) | DA | Classifier Alignment - Data Augmentation - Self-supervised Learning | - | ANN | SS | Motor Imagery Classification | Cross-Subject |
- | Deep BiLSTM neural network model for emotion detection using cross-dataset approach | 2022 | - | DG | Architecture Embedded | SEED - DEAP - Others | RNN | SS | Emotion Recognition | Cross-Dataset |
- | Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition | 2022 | [link](https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Inter_Subject_Contrastive_Learning_for_EEG) | DA | Instance Alignment | Others | ANN | MS | Visual Recognition | Cross-Subject |
- | Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold | 2022 | - | DA | Domain Alignment - Pseudo-label Training | BCI Competition | Non-Deep | SS | Motor Imagery Classification | Cross-Subject |
- | EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning | 2022 | - | DG | Architecture Embedded | SEED-IV - Others | CNN - RNN - Non-Deep | MS | Emotion Recognition | Cross-Dataset |
- | Domain adaptation for epileptic EEG classification using adversarial learning and Riemannian manifold | 2022 | - | DA-DG | Adversarial Feature Alignment - Multi Source Domain Alignment | Others | ANN - Autoencoder | MS | Seizure Detection/Prediction | Cross-Subject |
- | Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis | 2022 | - | DA-DG | Adversarial Feature Alignment - Feature Disentanglement - Data Manipulation | Others | Autoencoder | MS | Tinnitus Diagnosis | Cross-Dataset |
- | Exploiting Multiple EEG Data Domains with Adversarial Learning. | 2022 | [link](https://github.com/philipph77/acse-framework) | DG | Adversarial Training | SEED - DEAP - SEED-IV - DREAMER | ANN-CNN | MS | evaluating subjects’ mental states | Cross-Dataset |
- | Generator-based Domain Adaptation Method with Knowledge Free for Cross-subject EEG Emotion Recognition | 2022 | - | DA | Adversarial Feature Alignment | DEAP | GAN | SS | Emotion Recognition | Cross-Subject |
- | A Decomposition-Based Hybrid Ensemble CNN Framework for Improving Cross-Subject EEG Decoding Performance | 2022 | - | DG | Ensemble Learning - Architecture Embedded | Others | CNN | SS | Situation Awareness Recognition | Cross-Subject |
- | A deep subdomain associate adaptation network for cross-session and cross-subject EEG emotion recognition | 2022 | - | DA | Domain Alignment - Pseudo-label Training | SEED | CNN | SS | Emotion Recognition | Cross-Subject/Session |
- | Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment | 2022 | - | DA | Domain Alignment | Others | Non-Deep | MS | Mental Workload Classification | Cross-Session |
- | Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning | 2022 | - | DG | Meta-Learning - Self-supervised Learning | Others | CNN | SS | Sleep Scoring | Cross-Subject/Dataset |
- | From unsupervised to semi-supervised adversarial domain adaptation in EEG-based sleep staging | 2022 | - | DA | Adversarial Training - Pseudo-label Training | Others | RNN | SS | Sleep Stage Classification | Cross-Dataset |
- | Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces | 2022 | - | DG | Multi Source Domain Alignment | Others | CNN | SS | Motor Imagery Classification | Cross-Subject |
- | Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation | 2022 | [link](https://github.com/klean2050/EEG_CrossModal) | DA | Adversarial Training | DEAP | RNN | SS | Emotion Recognition | Cross-Subject |
- | Cross-Task Cognitive Workload Recognition Based on EEG and Domain Adaptation. | 2022 | - | DA | Domain Alignment - Pseudo-label Training | Others | Non-Deep | SS | Mental Workload Classification | Cross-Task/Subject |
- | Cross-Session EEG-Based Emotion Recognition Via Maximizing Domain Discrepancy | 2022 | - | DA | Adversarial Feature Alignment | SEED | CNN | SS | Emotion Recognition | Cross-Session |
- | Cross-Day EEG-Based Emotion Recognition Using Transfer Component Analysis | 2022 | - | DA | Domain Alignment | SEED - DEAP - Others | Non-Deep | SS | Emotion Recognition | Cross-Day |
- | Multi-view cross-subject seizure detection with information bottleneck attribution | 2022 | - | DG | Adversarial Training | Others | ANN - GAN | SS | Seizure Detection | Cross-Subject |
- | Cross-subject EEG-based emotion recognition through neural networks with stratified normalization | 2021 | - | DG | Data Manipulation | SEED | ANN | MS | Emotion Recognition | Cross-Subject |
- | Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification. | 2021 | [link](https://github.com/ziyujia/mstgcn) | DG | Adversarial Training | Others | Attention - Graph | SS | Sleep Stage Classification | Cross-Subject |
- | A prototype-based SPD matrix network for domain adaptation EEG emotion recognition | 2021 | - | DA | Adversarial Feature Alignment | DEAP - DREAMER | CNN - GAN | MS | Emotion Recognition | Cross-Subject |
- | Deep Learning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals | 2021 | - | DG | Architecture Embedded | Others | CNN | SS | Epileptic Seizure Prediction | Cross-Subject |
- | Plug-and-play domain adaptation for cross-subject eeg-based emotion recognition | 2021 | - | DA-DG | Instance Alignment - Feature Disentanglement - Ensemble Learning | SEED | RNN - Attention | MS | Emotion Recognition | Cross-Subject |
- | Two-Level Domain Adaptation Neural Network for EEG-Based Emotion Recognition | 2021 | - | DA | Adversarial Feature Alignment - Domain Alignment | SEED | CNN | MS | Emotion Recognition | Cross-Day/Subject |
- | Cross-subject EEG emotion recognition with self-organized graph neural network | 2021 | [link](https://github.com/tailofcat/SOGNN) | DG | Architecture Embedded | SEED - SEED-IV | Graph | SS | Emotion Recognition | Cross-Subject |
- | Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition. | 2021 | - | DA - DG | Domain Alignment - Pseudo-label Training - Architecture embedded | Others | RNN | SS | Imagined Speech Recognition | Cross-Subject |
- | An EEG-based transfer learning method for cross-subject fatigue mental state prediction | 2021 | - | DA | Adversarial Feature Alignment | Others | ANN | SS | Fatigue Mental State Prediction | Cross-Subject |
- | EEGNet with ensemble learning to improve the cross-session classification of SSVEP based BCI from Ear-EEG | 2021 | - | DG | Ensemble Learning | Others | CNN | SS | SSVEP-based BCI Classification | Cross-Session |
- | Dynamic Joint Domain Adaptation Network for Motor Imagery Classification | 2021 | - | DA | Adversarial Feature Alignment - Pseudo-label Training | BCI Competition | CNN | SS | Motor Imagery Classification | Cross-Session |
- | Subject-Invariant EEG Representation Learning For Emotion Recognition | 2021 | [link](https://github.com/philipph77/acse-framework) | DA | Adversarial Feature Alignment | Others | CNN | SS | Emotion Recognition | Cross-Subject/Dataset |
- | MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition | 2021 | [link](https://github.com/VoiceBeer/MS-MDA) | DA | Domain Alignment - Classifier Alignment | SEED - SEED-IV | ANN | MS | Emotion Recognition | Cross-Subject/Session |
- | Subject matching for cross-subject eeg-based recognition of driver states related to situation awareness | 2021 | - | DA | Instance Alignment | Others | CNN | SS | Situation Awareness Recognition | Cross-Subject |
- | A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition | 2021 | - | DA | Adversarial Feature Alignment - Instance Alignment | SEED | CNN | SS | Emotion Recognition | Cross-Subject |
- | Multi-Source Co-adaptation for EEG-based emotion recognition by mining correlation information | 2021 | - | DA | Instance Alignment - Pseudo-label Training | SEED - DEAP | ANN | MS | Emotion Recognition | Cross-Subject/Dataset |
- | Label-Based Alignment Multi-Source Domain Adaptation for Cross-Subject EEG Fatigue Mental State Evaluation | 2021 | - | DA | Domain Alignment - Classifier Alignment | Others | ANN - Non-Depp | MS | Mental State Prediction | Cross-Subject |
- | Multi-source signal alignment and efficient multi-dimensional feature classification in the application of EEG-based subject-independent drowsiness detection | 2021 | [link](https://github.com/shenmusmart/MSSA-TN) | DA | Instance Alignment | Others | Non-Deep | MS | Drowsiness Recognition | Cross-Subject |
- | An adversarial discriminative temporal convolutional network for EEG-based cross-domain emotion recognition. | 2021 | - | DA | Adversarial Feature Alignment | DEAP - DREAMER | CNN | SS | Emotion Recognition | Cross-Subject/Dataset |
- | Domain Adaptation for Cross-Subject Emotion Recognition by Subject Clustering | 2021 | - | DA | Preprocessing | DEAP | ANN | MS | Emotion Recognition | Cross-Subject |
- | Semi-Supervised Contrastive Learning for Generalizable Motor Imagery EEG Classification | 2021 | - | DA - DG | Pseudo-label Training - Adversarial Training - Data Augmentation - Self-supervised Learning | BCI Competition | CNN | SS | Motor Imagery Classification | Cross-Subject/Session |
- | Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization | 2021 | - | DG | Adversarial Training | SEED - DEAP | Autoencoder | SS | Emotion Recognition | Cross-Subject |
- | Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition. | 2021 | - | DG | Self-supervised Learning | SEED - Others | CNN | MS | Emotion Recognition | Cross-Subject |
- | EEG-Based Emotion Recognition via Joint Domain Adaptation and Semi-supervised RVFL Network | 2021 | - | DA | Domain Alignment - Pseudo-label Training - Data Augmentation | SEED-IV | - | SS | Emotion Recognition | Cross-Session |
- | Subject Adaptive EEG-Based Visual Recognition | 2021 | [link](https://github.com/DeepBCI/Deep-BCI) | DA | Domain Alignment | Others | RNN | MS | Visual Recognition | Cross-Subject |
- | Cross-subject And Cross-device Wearable EEG Emotion Recognition Using Frontal EEG Under Virtual Reality Scenes | 2021 | - | DA | Domain Alignment | Others | Graph | SS | Emotion Recognition | Cross-Subject/Device |
- | Seizure prediction in EEG signals using STFT and domain adaptation. | 2021 | - | DA | Adversarial Feature Alignment - Domain Alignment | Others | Non-Deep | SS | Seizure Prediction | Cross-Subject |
- | Cross-Subject EEG Emotion Recognition Using Domain Adaptive Few-Shot Learning Networks | 2021 | - | DA | Domain Alignment | SEED - DEAP | CNN - Attention-based | SS | Emotion Recognition | Cross-Subject |
- | Cross-subject EEG-based Emotion Recognition Using Adversarial Domain Adaptation with Attention Mechanism | 2021 | - | DA | Adversarial Feature Alignment | SEED | Attention-based - Graph | MS | Emotion Recognition | Cross-Subject |
- | Cross-subject electroencephalogram emotion recognition based on maximum classifier discrepancy | 2021 | - | DA | Adversarial Feature Alignment | SEED | GAN | SS | Emotion Recognition | Cross-Subject/Session |
- | Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks. | 2021 | - | DG | Architecture Embedded | Others | CNN | SS | Decoding Visual Imagery | Cross-Subject |
- | Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability | 2021 | - | DG | Self-supervised Learning | Others | CNN | SS | Behavioral State Estimation | Cross-Subject/Session/Dataset |
- | Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface | 2021 | - | DG | Data Augmentation | BCI Competition | GAN | MS | Motor Imagery Classification | Cross-Subject |
- | EEG emotion Enhancement using Task-specific Domain Adversarial Neural Network | 2021 | - | DA | Adversarial Feature Alignment | SEED | ANN | SS | Emotion Recognition | Cross-Subject/Phase |
- | Single-channel EEG based insomnia detection with domain adaptation. | 2021 | - | DA | Adversarial Feature Alignment | Others | RNN | MS | Sleep Stage Classification | Cross-Dataset |
- | ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training | 2021 | [link](https://github.com/emadeldeen24/ADAST) | DA | Adversarial Feature Alignment - Pseudo-label Training | Others | CNN - Attention-based | SS | Sleep Stage Classification | Cross-Dataset |
- | Multi-Branch Network for Cross-Subject EEG-based Emotion Recognition | 2021 | - | DA | Instance Alignment | SEED | CNN | SS | Emotion Recognition | Cross-Subject |
- | Coupled Projection Transfer Metric Learning for Cross-Session Emotion Recognition from EEG | 2021 | - | DA | Domain Alignment - Pseudo-label Training | SEED-IV | Non-Deep | SS | Emotion Recognition | Cross-Session/Trial |
- | A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition | 2021 | - | DG | Meta Learning | SEED | RNN | SS | Emotion Recognition | Cross-Subject |
- | Wasserstein-Distance-Based Multi-Source Adversarial Domain Adaptation for Emotion Recognition and Vigilance Estimation | 2021 | - | DA | Adversarial Feature Alignment | SEED - SEED-VIG | - | SS | Vigilance Estimation | Cross-Subject |
- | Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images | 2021 | - | DA - DG | Domain Alignment - Data Manipulation | Others | CNN - Attention-based | SS | Workload Classification | Cross-Subject |
- | Learning invariant representations from EEG via adversarial inference | 2020 | - | DG | Adversarial Training | Others | CNN | SS | Motor Imagery Classification | Cross-Subject |
- | Subject-Aware Contrastive Learning for Biosignals | 2020 | - | DG | Data Augmentation - Self-supervised Learning | Others | Autoencoder - Non-deep | SS | EEG Decoding | Cross-Subject |
- | Fusion convolutional neural network for cross-subject EEG motor imagery classification | 2020 | - | DG | Ensemble Learning | Others | CNN | SS | Motor Imagery Classification | Cross-Subject |
- | Deep representation-based domain adaptation for nonstationary EEG classification. | 2020 | - | DA | Adversarial Feature Alignment - Pseudo-label Training | BCI Competition - Others | CNN | SS | Motor Imagery Classification | Cross-Subject |
- | Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding | 2020 | - | DA | Adversarial Feature Alignment | Others | CNN | SS | Motor Imagery Classification | Cross-Subject |
- | Meta learn on constrained transfer learning for low resource cross subject EEG classification | 2020 | - | DG | Meta Learning | SEED - DEAP - BCI Competition | - | MS | Motor Imagery Classification | Cross-Subject |
- | Supervised domain generalization for integration of disparate scalp EEG datasets for automatic epileptic seizure detection | 2020 | - | DG | Multi Source Domain Alignment | Others | CNN - RNN | MS | Epileptic Seizure Prediction | Cross-Dataset |
- | Tensor-based EEG network formation and feature extraction for cross-session driving drowsiness detection | 2020 | - | DA | Domain Alignment | Others | Non-Deep | SS | Driving Drowsiness Detection | Cross-Session |
- | Data Augmentation for Domain-Adversarial Training in EEG-based Emotion Recognition | 2020 | - | DA | Adversarial Feature Alignment | DEAP | Non-Deep | SS | Emotion Recognition | Cross-Subject/Session/Dataset |
- | Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity | 2019 | - | DA | Adversarial Feature Alignment | SEED - DEAP | ANN | SS | Emotion Recognition | Cross-Subject/Session |
- | A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis | 2019 | [link](https://github.com/dalinzhang/CRAM) | DG | Multi Source Domain Alignment | BCI Competition | CNN - Attention-based | SS | Motor Imagery Classification | Cross-Subject |
- | Generalizing to unseen domains via distribution matching | 2019 | [link](https://github.com/belaalb/G2DM) | DG | Adversarial Training | SEED | - | MS | Emotion Recognition | Cross-Subject |
- | A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition | 2019 | - | DA | Adversarial Feature Alignment | SEED - SEED-IV - Others | RNN | SS | Emotion Recognition | Cross-Subject |
- | Multi-method fusion of cross-subject emotion recognition based on high-dimensional EEG features | 2019 | - | DG | Feature selection | SEED - DEAP | Non-Deep | SS | Emotion Recognition | Cross-Subject |
- | Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces | 2019 | [link](https://github.com/chamwen/MEKT) | DA | Domain Alignment - Pseudo-label Training | Others | - | SS | Motor Imagery Classification | Cross-Subject |
- | EEG-based driver drowsiness estimation using feature weighted episodic training | 2019 | - | DG | Feature Weighting | Others | ANN | MS | Drowsiness Recognition | Cross-Subject |
- | Cross-subject EEG signal recognition using deep domain adaptation network | 2019 | - | DA | Domain Alignment | BCI Competition | CNN | SS | Motor Imagery Classification | Cross-Subject |
- | Reducing the Subject Variability of EEG Signals with Adversarial Domain Generalization | 2019 | - | DG | Adversarial Training | SEED - SEED-VIG | ANN | MS | Emotion Recognition | Cross-Subject |
- | EEG-based cross-subject mental fatigue recognition | 2019 | - | DA | Domain Alignment | Others | CNN | SS | Mental Fatigue Recognition | Cross-Subject |
- | Subject adaptation network for EEG data analysis | 2019 | - | DG | Adversarial Training | Others | GAN | SS | VIgilance Estimation | Cross-Subject/Session |
- | Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI | 2019 | [link](https://github.com/eunjin93/SICR_BCI) | DA | Feature Disentanglement | Others | ANN | SS | Motor Imagery Classification | Cross-Subject |
- | Cross-subject statistical shift estimation for generalized electroencephalography-based mental workload assessment | 2019 | - | DA | Data Manipulation | Others | - | MS | Mental Workload Assessment | Cross-Subject |
- | Domain adaptation with optimal transport improves EEG sleep stage classifiers. | 2018 | - | DA | Instance Alignment | Others | CNN | SS | Sleep Stage Classification | Cross-Dataset |
- | Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. | 2017 | - | DG | Multi Source Domain Alignment | Others | Non-Deep | SS | Epileptic Seizure Prediction | Cross-Subject |
- | Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition. | 2016 | - | DA | Domain Alignment | SEED | Autoencoder | SS | Emotion Recognition | Cross-Subject/Session |
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