STFSFormer: Integrating Neuronal Dynamics and Full-Spike Attention for Energy-Efficient EEG Modeling

Efficient electroencephalography (EEG) modeling requires representations that retain rapid signal variations while capturing temporal dependencies under limited energy budgets. Spiking neural networks provide an energy-efficient computing substrate, but conventional leaky integrate-and-fire dynamics can attenuate rapid changes through temporal smoothing, while global attention incurs quadratic computational cost in sequence length. We introduce STFSFormer, a spiking Transformer that integrates transient-sensitive neuronal dynamics with local full-spike attention for energy-efficient EEG modeling. A high-frequency-enhanced leaky integrate-and-fire neuron combines a differential synaptic pathway with refractory feedback to emphasize input changes and regulate repeated firing. Parallel encoding branches with distinct decay profiles capture complementary temporal responses. Spatial-temporal full-spike attention integrates these representations using binary queries, keys, and values within local temporal blocks. Its softmax-free formulation selects the cheaper of two equivalent multiplication orders based on block length and head dimension, with attention cost scaling linearly in sequence length for fixed block size and feature dimension. Experiments on SEED, SEED-V, and DREAMER demonstrate competitive emotion recognition performance. Under the evaluated input configuration, STFSFormer uses 1.80 million parameters and 0.46 billion multiply–accumulate operations per segment. A consistent synthesis-based FPGA evaluation estimates whole-model inference power at 1.2 W and energy consumption at 1.5 mJ per segment, corresponding to 50.0% and 44.4% lower energy than the implemented SpikingGCN and SCNN baselines, respectively. These results demonstrate a favorable balance between recognition performance, computational cost, and energy-efficient inference.