ArticleEntropy (Basel, Switzerland)2022
A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding.
Article in Entropy (Basel, Switzerland), 2022. 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.
- RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.PloS one · 2026Article
- Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation.PloS one · 2025Article
- Multiresolution feature fusion for smart diagnosis of schizophrenia in adolescents using EEG signals.Cognitive neurodynamics · 2024Article
- A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network.Sensors (Basel, Switzerland) · 2024Article
- Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals.Frontiers in bioengineering and biotechnology · 2024Article
- [Recognition of motor imagery electroencephalogram based on flicker noise spectroscopy and weighted filter bank common spatial pattern].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion.Brain sciences · 2023Article
- Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.Frontiers in neuroscience · 2023Article
- A Parallel Feature Fusion Network Combining GRU and CNN for Motor Imagery EEG Decoding.Brain sciences · 2022Article
- Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
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3 authors.
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Abstract
With the development of technology and the rise of the meta-universe concept, the brain-computer interface (BCI) has become a hotspot in the research field, and the BCI based on motor imagery (MI) EEG has been widely concerned. However, in the process of MI-EEG decoding, the performance of the decoding model needs to be improved. At present, most MI-EEG decoding methods based on deep learning cannot make full use of the temporal and frequency features of EEG data, which leads to a low accuracy of MI-EEG decoding. To address this issue, this paper proposes a two-branch convolutional neural network (TBTF-CNN) that can simultaneously learn the temporal and frequency features of EEG data. The structure of EEG data is reconstructed to simplify the spatio-temporal convolution process of CNN, and continuous wavelet transform is used to express the time-frequency features of EEG data. TBTF-CNN fuses the features learned from the two branches and then inputs them into the classifier to decode the MI-EEG. The experimental results on the BCI competition IV 2b dataset show that the proposed model achieves an average classification accuracy of 81.3% and a kappa value of 0.63. Compared with other methods, TBTF-CNN achieves a better performance in MI-EEG decoding. The proposed method can make full use of the temporal and frequency features of EEG data and can improve the decoding accuracy of MI-EEG.
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