Neuro-SPO: Physically-Grounded and Affect-Aligned EEG-to-Keyword Decoding

Abstract

Decoding semantic information from non-invasive EEG remains a formidable challenge in Brain-Computer Interfaces due to the low signal-to-noise ratio and the complex biophysics of neural dynamics. While large-scale pre-trained models like Whisper provide powerful generic temporal representations, they lack the domain-specific biophysical constraints essential for EEG. Conversely, pure physical operators capture topology but miss the rich semantic priors. To bridge this gap, we propose the Neural Spectral-Physical Operator (Neuro-SPO), a robust framework that reframes EEG-to-Keyword decoding as a continuous representation learning problem. Neuro-SPO synergizes two complementary paradigms: a Pre-trained Temporal Operator derived from the Whisper encoder to extract high-level temporal semantics, and a novel Neural Holistic Physics Mixer (Neuro-HPM) that injects domain-specific physical constraints via dynamic spectral graph interactions. By fusing these streams, Neuro-SPO ensures that the learned representations are both semantically rich and biophysically plausible. Furthermore, to address the ambiguity of neural signals caused by affective variance, we introduce a Structure-Regularized Optimization strategy, employing the Affective Modulation Alignment and Topology-Preserving Ranking Objective to rectify the latent decision space. Extensive experiments on the ZuCo and ChineseEEG benchmarks demonstrate that Neuro-SPO significantly outperforms state-of-the-art methods in ranking metrics and retrieval accuracy. The code is available at https://github.com/Dray-Xu/Neuro-SPO.