ReviewFrontiers in human neuroscience2021
Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review.
Review in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- Automated Cerebral Edema Detection using Electroencephalography in Post-cardiac Arrest Patients.Neurocritical care · 2026Article
- Data-Efficient EEG-Based Person Identification via Time-Interval-Based Multi-Positive Contrastive Learning.Sensors (Basel, Switzerland) · 2026Article
- Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.Brain informatics · 2026Article
- Motor imagery BCI enables more practical and user-friendly exoskeleton control than smartwatch for users with spinal cord injury: a preliminary study.Journal of neuroengineering and rehabilitation · 2026Article
- Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events.Sensors (Basel, Switzerland) · 2026Article
- EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection.Sensors (Basel, Switzerland) · 2026Article
- A survey on data augmentation for EEG-based emotion recognition and cognitive workload decoding.Frontiers in neuroscience · 2026Review
- Computational whole-body-exposome models for global precision brain health.Nature communications · 2025Article
- Diversity-sensitive brain clocks linked to biophysical mechanisms in aging and dementia.Nature. Mental health · 2025Article
- Enhancing Electroencephalogram-Based Prediction of Posttraumatic Stress Disorder Treatment Response Using Data Augmentation.Psychiatry investigation · 2025Article
- A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.Scientific data · 2025Article
- GEM-CRAP: a fusion architecture for focal seizure detection.Journal of translational medicine · 2025Article
- Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.Journal of applied clinical medical physics · 2025Article
- Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.Brain sciences · 2024Article
- EEG-based emotion recognition using a temporal-difference minimizing neural network.Cognitive neurodynamics · 2024Article
- Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.Frontiers in robotics and AI · 2024Article
- 25-year neuroimaging research on spoken language processing: a bibliometric analysis.Frontiers in human neuroscience · 2024Article
- EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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4 authors.
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Abstract
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
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