ArticleSensors (Basel, Switzerland)2019
EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Article
- Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.Scientific reports · 2026Article
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- Self-supervised data-driven approach defines pathological high-frequency oscillations in epilepsy.Epilepsia · 2025Article
- Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.Biological cybernetics · 2024Article
- Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.Frontiers in neuroscience · 2024Article
- Toward metacognition: subject-aware contrastive deep fusion representation learning for EEG analysis.Biological cybernetics · 2023Article
- Decoding motor imagery with a simplified distributed dipoles model at source level.Cognitive neurodynamics · 2023Article
- Neuropsychological Evidence Underlying Counterclockwise Bias in Running: Electroencephalography and Functional Magnetic Resonance Imaging Studies of Motor Imagery.Behavioral sciences (Basel, Switzerland) · 2023Article
- Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.Sensors (Basel, Switzerland) · 2023Article
- An improved model using convolutional sliding window-attention network for motor imagery EEG classification.Frontiers in neuroscience · 2023Article
- Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.Frontiers in neuroscience · 2023Article
- Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed Using Convolutional Neural Networks.Brain sciences · 2022Article
- Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network.Micromachines · 2022Article
- A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding.Entropy (Basel, Switzerland) · 2022Article
- Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.PloS one · 2022Article
- Attention-Based DSC-ConvLSTM for Multiclass Motor Imagery Classification.Computational intelligence and neuroscience · 2022Article
- Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art.
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Registered trials
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.