ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2022
On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- An intelligent EEG-based ensemble framework for communication assistance in Locked-In Syndrome patients.Scientific reports · 2026Article
- Dual-Manifold Contrastive Learning for Robust and Real-Time EEG Motor Decoding.Sensors (Basel, Switzerland) · 2026Article
- Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces.Scientific reports · 2025Article
- A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue.Frontiers in neuroscience · 2025Article
- A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.Scientific data · 2024Article
- Thought-Controlled Computer Applications: A Brain-Computer Interface System for Severe Disability Support.Sensors (Basel, Switzerland) · 2024Article
- An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.Brain sciences · 2024Article
- Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.PNAS nexus · 2024Article
- Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control.IEEE transactions on bio-medical engineering · 2024Article
- PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task.Frontiers in human neuroscience · 2024Article
- Recognize enhanced temporal-spatial-spectral features with a parallel multi-branch CNN and GRU.Medical & biological engineering & computing · 2023Article
- The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets.Frontiers in human neuroscience · 2023Article
- Status of deep learning for EEG-based brain-computer interface applications.Frontiers in computational neuroscience · 2022Review
- Immediate effects of short-term meditation on sensorimotor rhythm-based brain-computer interface performance.Frontiers in human neuroscience · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and resulted in multiple models. Finding the best performing models among them would be beneficial for designing better BCI systems and classifiers going forward. However, it is difficult to directly compare performance of various models through the original publications, since the datasets used to test the models are different from each other, too small, or even not publicly available. In this work, we selected five MI-EEG deep classification models proposed recently: EEGNet, Shallow & Deep ConvNet, MB3D and ParaAtt, and tested them on two large, publicly available, databases with 42 and 62 human subjects. Our results show that the models performed similarly on one dataset while EEGNet performed the best on the second with a relatively small training cost using the parameters that we evaluated.
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