{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T19:10:12Z","timestamp":1780945812895,"version":"3.54.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivations<\/jats:title>\n                    <jats:p>Electroencephalography (EEG) is a non-invasive method that records brain electrical activity from scalp electrodes, offering millisecond temporal resolution but limited spatial detail due to sparse sensor layouts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present DiBiMa-EEGSR, a bidirectional Mamba-2 diffusion framework for spatio-temporal EEG super-resolution that reconstructs high-resolution signals from standard low-density recordings without additional hardware. The method formulates super-resolution as conditional generative inference and integrates a diffusion process with a bidirectional state-space backbone to model long-range temporal dependencies with linear complexity. Conditioning on low-resolution inputs, electrode positions and task labels enables anatomically coherent and context-aware reconstruction. A one-step sampling strategy substantially reduces inference time while preserving fidelity. Across two public benchmarks, the approach improves reconstruction accuracy, spatial coherence and spectral preservation over convolutional, transformer-based and prior diffusion models in both spatial and temporal upsampling tasks, providing a scalable pathway toward high-resolution electrophysiological imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code to reproduce ablation experiments, training and evaluation of the proposed BiMa and DiBiMa EEGSR models are available at https:\/\/github.com\/UgoLomoio\/DiBiMa-EEGSR.git. Model weights are available at https:\/\/huggingface.co\/Ugo96\/DiBiMa-EEGSR while an interactive demo for EEG spatial super-resolution using our models can be found at https:\/\/huggingface.co\/spaces\/Ugo96\/DiBiMa-EEGSR-Demo.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag169","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:20:32Z","timestamp":1776079232000},"source":"Crossref","is-referenced-by-count":1,"title":["Bidirectional Mamba-2 boosts EEG super-resolution via regression and diffusion"],"prefix":"10.1093","volume":"42","author":[{"given":"Ugo","family":"Lomoio","sequence":"first","affiliation":[{"name":"Department of Surgical and Medical Sciences, Magna Graecia University , Catanzaro 88100,","place":["Italy"]},{"name":"DIMES, University of Calabria , Rende 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