# Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

arXiv: https://arxiv.org/abs/2604.27033 · HTML: https://mlsanity.com/paper/2604.27033/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 95 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 30 of 30)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series](https://mlsanity.com/paper/2310.14017/) (2023)
- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification](https://mlsanity.com/paper/2405.19363/) (2024)
- [Epileptic Seizures Detection Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2007.01276/) (2020)
- [ManyDG: Many-domain Generalization for Healthcare Applications](https://mlsanity.com/paper/2301.08834/) (2023)
- [Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods](https://mlsanity.com/paper/2212.08744/) (2022)
- [Neural decoding from stereotactic EEG: accounting for electrode variability across subjects](https://mlsanity.com/paper/2411.10458/) (2024)
- [Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection](https://mlsanity.com/paper/1909.10868/) (2019)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification](https://mlsanity.com/paper/2311.05415/) (2023)
- [BrainOOD: Out-of-distribution Generalizable Brain Network Analysis](https://mlsanity.com/paper/2502.01688/) (2025)
- [Repurposing Foundation Model for Generalizable Medical Time Series Classification](https://mlsanity.com/paper/2410.03794/) (2024)
- [Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning](https://mlsanity.com/paper/2411.16155/) (2024)
- [mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup](https://mlsanity.com/paper/2504.07987/) (2025)
- [The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding](https://mlsanity.com/paper/2404.06868/) (2024)
- [FAPEX: Fractional Amplitude-Phase Expressor for Robust Cross-Subject Seizure Prediction](https://mlsanity.com/paper/2511.03263/) (2025)

## Built on by (most cited first, 3 of 3)

- [Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics](https://mlsanity.com/paper/2608.16569/) (2026)
- [Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decoding](https://mlsanity.com/paper/2609.24233/) (2026)
- [Structured Visual Target Learning For Cross-Subject eeg-to-image retrieval](https://mlsanity.com/paper/2609.36971/) (2026)

## Abstract (arXiv)

Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject setting as a multi-source domain problem and delineate the rigorous, subject-independent evaluation protocols required for valid assessment. Central to this survey is a systematic taxonomy of the current literature into discrete methodological families, including feature alignment, adversarial learning, feature disentanglement, and contrastive learning. We conclude by examining three critical elements for advancing robust, real-world decoding: the theoretical limitations of current methodologies, the structural value of subject identity, and the emergence of EEG foundation models.