# Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods

arXiv: https://arxiv.org/abs/2212.08744 · HTML: https://mlsanity.com/paper/2212.08744/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 222 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization](https://mlsanity.com/paper/1510.04373/) (2015)
- [Domain-Adversarial Neural Networks](https://mlsanity.com/paper/1412.4446/) (2014)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition](https://mlsanity.com/paper/1906.01704/) (2019)
- [MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition](https://mlsanity.com/paper/2107.07740/) (2021)
- [On The Effects Of Data Normalisation For Domain Adaptation On EEG Data](https://mlsanity.com/paper/2210.01081/) (2022)
- [Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces](https://mlsanity.com/paper/1910.05878/) (2019)
- [Learning Domain-Invariant Subspace using Domain Features and Independence Maximization](https://mlsanity.com/paper/1603.04535/) (2016)
- [Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress](https://mlsanity.com/paper/1912.10528/) (2019)

## Built on by (most cited first, 17 of 17)

- [Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning](https://mlsanity.com/paper/2401.13796/) (2024)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [CrossPT-EEG: A Benchmark for Cross-Participant and Cross-Time Generalization of EEG-based Visual Decoding](https://mlsanity.com/paper/2406.07151/) (2024)
- [Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods](https://mlsanity.com/paper/2604.27033/) (2026)
- [Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework](https://mlsanity.com/paper/2505.18175/) (2025)
- [Affect and Effect: Limitations of regularisation-based continual learning in EEG-based emotion classification](https://mlsanity.com/paper/2601.07858/) (2026)
- [Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception](https://mlsanity.com/paper/2508.11691/) (2025)
- [Rethinking Generalized BCIs: Benchmarking 340,000+ Unique Algorithmic Configurations for EEG Mental Command Decoding](https://mlsanity.com/paper/2512.02978/) (2025)
- [Multimodal Functional Maximum Correlation for Emotion Recognition](https://mlsanity.com/paper/2512.23076/) (2025)
- [Local-Global Feature Fusion for Subject-Independent EEG Emotion Recognition](https://mlsanity.com/paper/2601.08094/) (2026)
- [Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data](https://mlsanity.com/paper/2603.28122/) (2026)
- [How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study](https://mlsanity.com/paper/2605.16953/) (2026)
- [BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series](https://mlsanity.com/paper/2605.22468/) (2026)
- [Assessing Region-Level EEG Contributions to Cognitive Workload Prediction](https://mlsanity.com/paper/2606.02598/) (2026)
- [Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders](https://mlsanity.com/paper/2606.24394/) (2026)
- [Subject-Level Heterogeneity in EEG Motor Imagery Decoding: A Large-Scale Benchmark and Portfolio-Based Reduction of the Search Space](https://mlsanity.com/paper/2607.22778/) (2026)
- [RETRACE: Resilience-Guided Trait-Conditioned Craving Estimation from Wearable Physiology in Opioid Use Disorder](https://mlsanity.com/paper/2608.14947/) (2026)

## Abstract (arXiv)

A systematic review on machine-learning strategies for improving generalizability (cross-subjects and cross-sessions) electroencephalography (EEG) based in emotion classification was realized. In this context, the non-stationarity of EEG signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. 418 papers were retrieved from the Scopus, IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment. Among these papers, 75 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject and cross-session validation strategy and making use of other biosignals as support were excluded. On the basis of the selected papers' analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion on the different ML approaches involved. The studies with the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.