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A survey on data augmentation for EEG-based emotion recognition and cognitive workload decoding.

Yunyu Zhu¹, Yueying Zhou¹, Pengpai Wang²

¹School of Mathematics and Systems Science, Liaocheng University, Liaocheng, China.

Frontiers in Neuroscience

|April 27, 2026

Summary

This summary is machine-generated.

This systematic review analyzes data augmentation techniques for deep learning models in emotion recognition and cognitive workload decoding using electroencephalography (EEG). It highlights challenges and provides references for enhancing model performance.

Keywords:

cognitive workload data augmentation deep learning electroencephalography (EEG)emotion

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Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Electroencephalography (EEG) is crucial for emotion recognition and cognitive workload decoding.
Deep learning models face challenges like data scarcity and limited generalization with EEG data.
Data augmentation (DA) is vital for addressing data scarcity in these applications.

Purpose of the Study:

To systematically review data augmentation methods for deep learning in EEG-based emotion recognition and cognitive workload decoding.
To summarize public EEG datasets, input representations, and deep learning classifiers.
To analyze the effectiveness of seven categories of DA methods in these specific tasks.

Main Methods:

Systematic literature review of relevant research.
Analysis of commonly used public EEG datasets and deep learning classifiers.
Categorization and evaluation of seven types of data augmentation techniques.

Main Results:

Identified common EEG datasets, input representations, and deep learning models.
Assessed the application and effectiveness of various DA methods.
Highlighted current challenges and future research directions in the field.

Conclusions:

Data augmentation is critical for improving deep learning model performance in EEG-based emotion recognition and cognitive workload decoding.
Further research is needed to address current challenges and optimize DA strategies.
This review offers valuable guidance for selecting and applying DA techniques.