The electroencephalogram （EEG）-based emotion recognition is a key technology in next-generation human-computer interaction and brain-computer interface （BCI） systems， with significant applications in national brain science initiatives and intelligent healthcare. However， the inherent non-stationarity， low signal-to-noise ratio， and scarcity of medical EEG samples present substantial challenges for existing deep learning methods in real-world scenarios. Most current approaches directly extract frequency-domain features from single-channel raw signals. This strategy not only risks misinterpreting high-frequency noise as informative features but also neglects the spatial synergy among different brain regions， limiting the model’s generalization ability. To address these issues， this paper proposes a synergy-first end-to-end EEG emotion recognition framework， introducing the synergy-frequency aggregation network （SFAN） for collaborative frequency-domain feature extraction based on aggregation convolutions. Departing from the conventional “frequency extraction followed by spatial fusion” paradigm， the SFAN first employs a channel aggregation convolution module to adaptively fuse multi-channel EEG signals， simulating dynamic inter-regional brain synergies to generate highly robust collaborative representations. Frequency-domain feature extraction and deep spatiotemporal modeling are then performed on these representations. This strategy effectively suppresses channel-level noise prior to frequency analysis and enriches the feature space through diverse aggregation patterns， alleviating overfitting under small-sample conditions. The SFAN achieves a classification accuracy of 97.42% on the SEED-Ⅳ dataset and 94.91% on the SEED-Ⅴ dataset. Experimental results demonstrate that the proposed method consistently outperforms existing approaches in cross-subject and cross-session emotion recognition tasks， validating the effectiveness of incorporating inter-regional brain collaboration prior to frequency-domain analysis for enhancing EEG emotion recognition robustness.