AdaRelate: Learning to Acquire Multiscale Evidence for EEG Emotion Recognition

Emotion-related EEG structure spans individual channels, geometry-defined regions, and relations between regions, yet the useful scale and target change with each recording. Existing static and sample-adaptive graphs generally instantiate a broad relational representation in one pass, before the value of its constituent computations is known. AdaRelate instead formulates recognition as reliability-anchored sequential hierarchical evidence acquisition. A training-fitted low-order anchor supplies initial class logits. A graph-constrained actor–critic then selects a REGION, CHANNEL, RELATION, or STOP action; only the selected shared expert is evaluated, and its evidence conditions the next decision. A strictly forward accumulator incorporates preceding windows within each trial. Across four datasets, the complete system exceeds the strongest reproduced mean for Accuracy, balanced accuracy, and macro-F1. A matched three-seed evaluation yields 66.81% Accuracy, against 65.87% for one-shot top-$k$ and 65.64% for no-RL routing. Removing the anchor reduces Accuracy by 7.42 points, while forward history contributes 6.06 points. Evidence-nulling diagnostics show that acquired evidence changes subsequent actions and stopping decisions, and four-dataset maps reveal distinct channel, region, and relation allocations. The results establish a hierarchical EEG interface in which a reliable prediction base and sequential reinforcement learning jointly support sample-conditioned emotion recognition.