Robust Resonate‑and‑Fire Neurons for Real-Time End‑to‑End EEG Emotion Recognition

Real-world applications of EEG analysis call for energy-efficient, low latency operation to enable scalable, real-time inference on streamed signals. Spiking Neural Networks (SNNs), through sparse, event-driven processing, can satisfy these demands, yet their benefits are rarely realised in practice as implementations stick to pipelines that feature dense, windowed computation. This includes SNNs for EEG emotion recognition, where the use of windowed spectral features hampers sparse computation, inflates latency, and coarsens temporal resolution. More elaborate neuron models can address these limitations: the Resonate-and-Fire (RF) model captures spectral features via oscillating membrane dynamics, but remains under-explored due to parameter sensitivity and instability. Existing RF formulations mitigate these issues at the cost of added complexity; we instead introduce a robust RF model, bolstered by data-driven initialisation, that enables reliable and efficient integration into SNNs, as evidenced by reduced computational cost and spike rates compared to other RF models. Leveraging our RF neurons to extract spectral features directly from raw EEG, we further present **SS-EEGNet**, a Spiking and Streaming EEGNet-based model for online end-to-end emotion recognition in real-time. Our approach enables causal, low-latency inference while preserving sparsity, achieving competitive accuracy alongside substantial efficiency gains.