LGFCNN: A synergistic framework integrating graph-based spatial filter and lightweight CNN for SSVEP recognition

Optimizing the feature representation and decoding efficiency of the steady-state visual evoked potentials (SSVEPs) is critical to enhancing the performance of neural signal decoding systems. Current deep learning models often overlook the physical topological information of EEG channels, resulting in suboptimal feature extraction and limited recognition performance. To address these challenges, this study proposes a synergistically designed SSVEP recognition framework to alleviate data insufficiency, improve the feature representation, and enhance decoding efficiency. Specifically, a slicing-and-scaling technique is adopted to improve the model generalization under limited-sample scenarios. A graph-based spatial filter leverages the topological relationships among EEG channels to suppress redundant information and enhance spatial feature quality. A lightweight convolutional neural network (CNN) with fewer parameters is developed to efficiently extract discriminative temporal–spatial features for accurate SSVEP classification. Experimental results on two public benchmark datasets and one self-collected dataset demonstrate that the proposed framework outperforms baseline deep learning models, yielding improvements of at least 6.8 %, 8.5 %, and 0.5 % in peak average classification accuracy, respectively. The maximum average information transfer rates (ITRs) achieved on the three datasets were 221.4 bits/min,106.7 bits/min, and 133.9 bits/min, respectively. By simultaneously reducing model complexity and improving decoding performance, the proposed framework offers an effective and promising approach for efficient neural signal decoding in SSVEP recognition.