Research on EEG-fMRI mapping methods for high spatiotemporal resolution brain-computer interfaces
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1.School of Computer Science and Technology, Changchun University of Science and Technology,Changchun 130022, China; 2.Jilin Provincial International Joint Research Center of Brain Informatics and Intelligence Science,Changchun 130022, China; 3.School of Computer Science and Technology, Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China; 4.School of Biomedical Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China;5.Medical Artificial Intelligence Center, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences,Shenzhen 518055, China

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TN911.7

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    Abstract:

    Existing BCI neurofeedback techniques often struggle to balance temporal and spatial resolution. Among mainstream neurofeedback methods, EEG offers millisecond-level temporal resolution but lacks precise spatial localization, whereas fMRI provides high spatial resolution but is constrained by second-level temporal delays. This trade-off in spatiotemporal resolution limits the clinical applicability of neurofeedback. To address this issue, this study proposes a hybrid wavelet neural network to model the complex nonlinear mapping between EEG signals and fMRI regional activity. The model employs parallel wavelet convolutional layers and one-dimensional convolutional layers to extract multi-resolution frequency-domain features and local time-domain features from EEG signals, respectively. A channel cross-attention mechanism is further introduced to capture nonlinear interactions between features, while a LSTM network models long-range temporal dependencies. Experimental results demonstrate that the proposed approach achieves high-precision prediction of fMRI regional dynamics across two independent datasets, significantly outperforming traditional linear models. This framework not only extends the modeling capacity of current neurofeedback “EFP” techniques but also provides a new pathway for developing neurofeedback and BCI systems with both high temporal and spatial resolution.

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  • Online: April 16,2026
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