Stage-Aligned Temporal Representation Learning for Motor Imagery EEG Decoding

Motor imagery (MI) EEG decoding relies on class-discriminative information encoded in the temporal evolution of sensorimotor-rhythm activity. Yet existing decoders largely treat temporal operators as generic architectural components, overlooking the distinct temporal learning objectives required by successive EEG representation stages. We formulate Stage-Aligned Temporal Representation Learning (SATRL), a framework that aligns distinct temporal operators with three stage-specific learning objectives. SATRL instantiates this principle through three complementary temporal operators: a multiscale temporal extractor that captures sensorimotor-rhythm responses from channel-wise EEG dynamics, a stacked small-kernel depthwise aggregator that composes local rhythm-related activations into stable short-window representations, and a context-guided dilated temporal module that integrates long-range evidence under trial-level context. SATRL elevates the recurring temporal-spatial design pattern into an explicit, principled, and experimentally testable operator-stage alignment framework. Experiments on public MI-EEG benchmarks demonstrate consistent improvements across both compact CNN and CNN-Transformer backbones. Under cross-session evaluations, SATRL improves the corresponding baselines by up to 5.26 and 10.24 percentage points, respectively, while achieving state-of-the-art classification accuracies on all datasets. Leave-one-subject-out evaluations further place SATRL in the top-performing tier, and stage-ablation studies consistently show that removing or changing any temporal stage degrades performance in all backbone types. Together, these results suggest SATRL as a principled framework for MI-EEG decoding.