ST-CoG-XAI: A Spectrotemporal Contrastive Generation Foundation Model for Explainable EEG Decoding

Abstract

Wearable electroencephalography (EEG) on flexible substrates enables continuous neuromonitoring but violates the fixed-montage assumption of conventional decoders: electrode geometry varies across devices, channels intermittently disappear, and task-relevant biomarkers drift over spectral and spatial dimensions. We present ST-CoG-XAI, a unified framework that delivers accurate and intrinsically interpretable decoding under these nonstationary conditions. The model begins with dual-stream encoder: an asymmetric convolutional network (ACN) converts time-domain windows into patch tokens, whereas a graph neural network (GNN) tokenizes frequency-band connectomes. These two token streams are fused and processed by a unified spectral–spatial–temporal synergy criss-cross transformer (UniSST-CCT) whose multihead attention is decomposed into spatial, temporal, and spectral groups, capturing cross-axis dependencies at only a constant-factor increase in complexity. Prior to supervision, the encoder is exposed to large unlabeled data through spectrotemporal contrastive generation (CoG), which alternates three mutually exclusive reconstruction tasks to align temporal and spectral representations. After CoG pretraining, supervised fine-tuning activates a weight-sharing second forward pass whose DeepLIFT-guided reattention explainable artificial intelligence (XAI) refines the encoder while preserving clinically meaningful patterns. Evaluated on seven clinical Brain–computer interface (BCI) benchmarks, covering neurodegenerative disorders, epilepsy detection, sleep staging, and event-related classification, ST-CoG-XAI matches or surpasses published state-of-the-art results, confirming robust generalization not only across heterogeneous EEG montages but also when operating with a limited or partial set of channels.