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.