To address the issue of limited channels and insufficient information collection in portable EEG devices, this study explores an EEG virtual channel signal generation network using a novel spatio-temporal feature fusion strategy. Based on the EEG signals from four frontal lobe channels, the network aims to generate virtual channel EEG signals for other 13 important brain regions. The architecture of the network is a two-dimensional convolutional neural network and it includes a parallel module for temporal and spatial domain feature extraction, followed by a feature fusion module. The public PRED+CT database, which includes multi-channel EEG signals from 119 subjects, was selected to verify the constructed network. The results showed that the average correlation coefficient between the generated virtual channel EEG signals and the original real signals was 0.6724, with an average absolute error of 3.9470. Furthermore, the 13 virtual channel EEG signals were combined with the original EEG signals of four brain regions and then used for anxiety classification with a support vector machine. The results indicate that the virtual EEG signals generated by the constructed network not only have a high degree of consistency with the real channel EEG signals but also significantly enhance the performance of machine learning algorithms for anxiety classification. This study effectively alleviates the problem of insufficient information acquisition by portable EEG devices with few channels.