ArticleSensors (Basel, Switzerland)2020
Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 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
- Enhancing Electroencephalogram-Based Prediction of Posttraumatic Stress Disorder Treatment Response Using Data Augmentation.Psychiatry investigation · 2025Article
- Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.Sensors (Basel, Switzerland) · 2025Review
- AMANet: a data-augmented multi-scale temporal attention convolutional network for motor imagery classification.Frontiers in neurorobotics · 2025Article
- Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.PloS one · 2025Article
- Duple-MONDNet: duple deep learning-based mobile net for motor neuron disease identification.Turkish journal of medical sciences · 2025Article
- Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.Frontiers in neuroinformatics · 2025Article
- On the role of generative artificial intelligence in the development of brain-computer interfaces.BMC biomedical engineering · 2024Review
- A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.Brain sciences · 2024Article
- EEGGAN-Net: enhancing EEG signal classification through data augmentation.Frontiers in human neuroscience · 2024Article
- Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.Frontiers in robotics and AI · 2024Article
- Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.Sensors (Basel, Switzerland) · 2023Article
- Recognize enhanced temporal-spatial-spectral features with a parallel multi-branch CNN and GRU.Medical & biological engineering & computing · 2023Article
- Generative adversarial networks in EEG analysis: an overview.Journal of neuroengineering and rehabilitation · 2023Review
- Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding.Frontiers in human neuroscience · 2023Article
- Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder.Frontiers in neuroscience · 2023Article
- Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.Scientific reports · 2022Article
- Article
- Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain-computer interface.Scientific reports · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extraction and classification based on MI signals. The decoding model based on deep neural networks (DNNs) has attracted significant attention in the field of MI signal processing. Due to the strict requirements for subjects and experimental environments, it is difficult to collect large-scale and high-quality electroencephalogram (EEG) data. However, the performance of a deep learning model depends directly on the size of the datasets. Therefore, the decoding of MI-EEG signals based on a DNN has proven highly challenging in practice. Based on this, we investigated the performance of different data augmentation (DA) methods for the classification of MI data using a DNN. First, we transformed the time series signals into spectrogram images using a short-time Fourier transform (STFT). Then, we evaluated and compared the performance of different DA methods for this spectrogram data. Next, we developed a convolutional neural network (CNN) to classify the MI signals and compared the classification performance of after DA. The Fréchet inception distance (FID) was used to evaluate the quality of the generated data (GD) and the classification accuracy, and mean kappa values were used to explore the best CNN-DA method. In addition, analysis of variance (ANOVA) and paired
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Registered trials
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