Yi Sun

Asynchronous EEG event detection is solved by a novel multiscale discrete spatiotemporal tokenization that captures both brief and sustained neural dynamics, outperforming prior methods across diverse datasets.

This work trains neural networks on synthesized frames to approach the optimum Bayes estimator for dense emitter localization and justifies the future work on training neural networks to achieve high-throughput large-FOV super spatiotemporal resolution SMLM.