NeuroIntentFormer: Evidence-Conditioned Temporal Aggregation for Motor Imagery Decoding in Stroke Rehabilitation

Decoding left- and right-hand motor imagery from stroke recordings can provide intention signals for rehabilitation-oriented interaction, but discriminative evidence varies across patients and trials. Beyond learning temporal representations, a decoder must determine how their content and class predictions should inform a joint decision. We formulate this step as evidence-conditioned temporal aggregation and introduce NeuroIntentFormer. The model constructs complementary temporal evidence and lets the evidence content and predictions jointly condition source aggregation and decision refinement. On seven held-out participants from a subject-disjoint 49-participant stroke cohort, NeuroIntentFormer achieves 85.53% subject-mean balanced accuracy (BAcc), with a participant-level bootstrap 95% confidence interval of [81.10%, 89.84%]. A parameter-matched ablation shows that evidence conditioning with joint decision refinement improves BAcc across all test trials by 1.76 percentage points (pp) over context-only routing. On OpenBMI, the same architecture reaches 82.03% subject-mean BAcc, with a 1.65 pp difference over MSVTNet. These results support the joint decision mechanism for offline motor imagery decoding.