ArticleSensors (Basel, Switzerland)2023
Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain-Computer Interfaces by Using Multi-Branch CNN.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.Brain sciences · 2026Article
- Noninvasive Intelligent Brain-Metasurface System for Real-Time Electromagnetic Wave Manipulation.Nanophotonics (Berlin, Germany) · 2026Article
- EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection.Sensors (Basel, Switzerland) · 2026Article
- Cross-variability decoding for motor imagery EEG signals: a comprehensive review.Frontiers in neuroscience · 2026Review
- Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.Scientific reports · 2025Article
- SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces.Frontiers in neuroscience · 2025Article
- A composite improved attention convolutional network for motor imagery EEG classification.Frontiers in neuroscience · 2025Article
- A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection.Frontiers in neuroscience · 2025Article
- Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.Frontiers in neuroscience · 2025Article
- CTNet: a convolutional transformer network for EEG-based motor imagery classification.Scientific reports · 2024Article
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3 authors.
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Abstract
A brain-computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the
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