ArticleFrontiers in human neuroscience2021
Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG.
Article 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 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- EEG-TriNet++: A Transformer-Guided Meta-Learning Framework for Robust and Generalizable Motor Imagery Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events.Sensors (Basel, Switzerland) · 2026Article
- Transformer architecture for diagnosing schizophrenia disabilities through EEG analysis.Frontiers in physiology · 2026Article
- Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.AIMS neuroscience · 2026Review
- DeepAttNet: deep neural network incorporating cross-attention mechanism for subject-independent mental stress detection in passive brain-computer interfaces using bilateral ear-EEG.Frontiers in human neuroscience · 2025Article
- Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.PloS one · 2025Article
- Targeting electroencephalography for alcohol dependence: A narrative review.CNS neuroscience & therapeutics · 2023Review
- Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Augmenting ECG Data with Multiple Filters for a Better Emotion Recognition System.Arabian journal for science and engineering · 2023Article
- A hybrid P300-SSVEP brain-computer interface speller with a frequency enhanced row and column paradigm.Frontiers in neuroscience · 2023Article
- Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI.Frontiers in human neuroscience · 2023Article
- CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation.Cyborg and bionic systems (Washington, D.C.) · 2023Article
- SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods.Frontiers in neuroscience · 2023Article
- Activation of the brain during motor imagination task with auditory stimulation.Frontiers in neuroscience · 2023Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (
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