MTDANet：面向运动想象脑机接口的轻量多尺度时序与距离正则线性注意力网络

Objective. Motor-imagery (MI) electroencephalography (EEG) offers a non-invasive route to brain–computer interaction, yet reliable single-trial decoding remains difficult because task-related patterns are weak, nonstationary, and distributed over multiple temporal scales. This study investigates whether a staged local-to-global model can integrate fine temporal–spatial structure with longer-range context without relying on unrestricted self-attention. Approach. We propose MTDANet, an architecture that combines multi-scale temporal–spatial feature extraction, factorized temporal consolidation, and distance-regularized recurrent attention. The model was evaluated on three public MI datasets under subject-specific and leave-one-subject-out (LOSO) protocols. Separately initialized models were also trained on P300 and steady-state visual evoked potential (SSVEP) datasets to examine broader architectural applicability. Participant-level paired analyses used two-sided paired tests with within-setting Holm correction. Main results. MTDANet achieved subject-specific accuracies of , , and on brain–computer interface (BCI) Competition IV 2a, BCI Competition IV 2b, and BNCI2014_002, respectively. Relative to the highest-mean baseline, the paired improvements were supported after within-setting Holm correction on BCI Competition IV 2a (3.56 percentage points, adjusted ) and 2b (1.62 points, adjusted ). Under LOSO evaluation, the corresponding accuracies were , , and . The 2.08-point BNCI2014_002 difference had a 95% confidence interval of 0.42–3.73 points and an adjusted value of 0.0387. These three comparisons did not remain significant in a broader 56-comparison Holm sensitivity analysis. The P300 and SSVEP models attained the highest observed mean accuracies. Significance. The results provide within-setting evidence for staged local-to-global temporal modeling in offline MI-EEG evaluations.