RSVP enables efficient EEG-based brain-computer interfaces, but single-trial decoding is difficult because of signal overlap and multicomponent entanglement. The team first built DisCo-Former, a Transformer-based framework with three priors-guided components: trend-periodicity disentanglement, channel-level embeddings that preserve the global temporal pattern, and contrastive learning that exploits target-adjacent non-targets. DisCo-Former surpassed existing approaches, yet analysis revealed a consistent attention collapse, with attention maps becoming nearly uniform and value projection weights shrinking toward 0.
Removing the Transformer encoder while keeping every other module yields DisCo-MLP, a purely multilayer perceptron variant. Across 2 datasets and three evaluation regimes, DisCo-MLP matched or outperformed its Transformer-based counterpart, with within-subject mean AUCs of approximately 0.94 to 0.98 that consistently exceeded strong baselines, the authors report. They conclude that for RSVP-EEG decoding, effectiveness stems less from architectural complexity than from modeling the structure of the signal, and that simplicity motivated by paradigm-specific neurophysiological priors offers a practical path to state-of-the-art performance.