ArticleHeliyon2022
Data augmentation strategies for EEG-based motor imagery decoding.
Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- MEG Neural Decoding Pipeline: The Issues Residing Within The Data and Methods to Improve Your Decoding Accuracy.Annals of biomedical engineering · 2026Article
- Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events.Sensors (Basel, Switzerland) · 2026Article
- Leveraging Cross-Subject Transfer Learning and Signal Augmentation for Enhanced RGB Color Decoding from EEG Data.Brain sciences · 2026Article
- EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in motor imagery and execution.GigaScience · 2026Article
- Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.AIMS neuroscience · 2026Review
- Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning.Journal of neuroengineering and rehabilitation · 2025Article
- Data-Centric AI for EEG-Based Emotion Recognition: Noise Filtering and Augmentation Strategies.Bioengineering (Basel, Switzerland) · 2025Article
- Motor imagery EEG signal classification using novel deep learning algorithm.Scientific reports · 2025Article
- Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.Sensors (Basel, Switzerland) · 2025Review
- Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.Frontiers in neuroinformatics · 2025Article
- Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM.Brain sciences · 2024Article
- Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.Respiratory research · 2024Article
- Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN.Heliyon · 2024Article
- Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose.Sensors (Basel, Switzerland) · 2023Article
- Get a New Perspective on EEG: Convolutional Neural Network Encoders for Parametric t-SNE.Brain sciences · 2023Article
- Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.Sensors (Basel, Switzerland) · 2023Article
- Data Augmentation Effects on Highly Imbalanced EEG Datasets for Automatic Detection of Photoparoxysmal Responses.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The wide use of motor imagery as a paradigm for brain-computer interfacing (BCI) points to its characteristic ability to generate discriminatory signals for communication and control. In recent times, deep learning techniques have increasingly been explored, in motor imagery decoding. While deep learning techniques are promising, a major challenge limiting their wide adoption is the amount of data available for decoding. To combat this challenge, data augmentation can be performed, to enhance decoding performance. In this study, we performed data augmentation by synthesizing motor imagery (MI) electroencephalography (EEG) trials, following six approaches. Data generated using these methods were evaluated based on four criteria, namely - the accuracy of prediction, the Frechet Inception distance (FID), the t-distributed Stochastic Neighbour Embedding (t-SNE) plots and topographic head plots. We show, based on these, that the synthesized data exhibit similar characteristics with real data, gaining up to 3% and 12% increases in mean accuracies across two public datasets. Finally, we believe these approaches should be utilized in applying deep learning techniques, as they not only have the potential to improve prediction performances, but also to save time spent on subject data collection.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.