ArticleFrontiers in neuroscience2023
Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.
Article in Frontiers in neuroscience, 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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Who cites it
10 citing papers in PubMed.
- Application and prospects of brain-computer interface technology for motor function reconstruction after brachial plexus injury.Annals of medicine · 2026Review
- Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.Journal of neuroengineering and rehabilitation · 2026Article
- A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.Frontiers in human neuroscience · 2025Article
- [Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- 3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.Frontiers in neurorobotics · 2024Article
- Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.Frontiers in neuroscience · 2024Article
- Coupling Effects of Cross-Corticomuscular Association during Object Manipulation Tasks on Different Haptic Sensations.NeuroSci · 2023Article
- Brain-Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review).Sovremennye tekhnologii v meditsine · 2023Review
- TSPNet: a time-spatial parallel network for classification of EEG-based multiclass upper limb motor imagery BCI.Frontiers in neuroscience · 2023Article
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6 authors.
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Abstract
Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.
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