{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:19:27Z","timestamp":1787980767113,"version":"build-2784847793"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background\/Objectives: Electroencephalography (EEG), with its high temporal resolution, non-invasiveness, and cost-effectiveness, provides a suitable modality for investigating task-related signal patterns associated with Visual Sustained Attention (VSA) and Visual Internally Directed Cognition (VIDC), but cross-subject generalization remains challenging in reduced-channel settings. This study evaluated PDC-Net under an offline GPU setting. Methods: PDC-Net uses a Temporal Representation Adaptation Block (TRAB) for local temporal transformation and feature-channel recalibration and a Cross-Branch Gated Mamba Interaction (CGMI) module for input-dependent bilateral information exchange and long-range sequence modeling. Results: Under the strict LOSO cross-validation setting, PDC-Net achieved an average decoding accuracy of 76.76%, representing the highest mean accuracy among the 11 evaluated models. The model also maintained a favorable balance between cross-subject decoding performance and computational cost under the evaluated offline GPU setting. Conclusions: These findings support the feasibility of offline, subject-independent VSA\/VIDC decoding from dual-channel Fp1\/Fp2 EEG under the evaluated controlled conditions and provide a basis for subsequent external, cross-session, online, and hardware-specific evaluation.<\/jats:p>","DOI":"10.3390\/brainsci16090915","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:47:13Z","timestamp":1787849233000},"page":"915","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PDC-Net: A Prefrontal Dual-Channel Network with Gated Mamba Interaction for Cross-Subject Visual Attentional State Decoding"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5593-4611","authenticated-orcid":false,"given":"Tianyuan","family":"Niu","sequence":"first","affiliation":[{"name":"School of Hebei Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China"},{"name":"State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China"},{"name":"Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China"},{"name":"Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7540-7309","authenticated-orcid":false,"given":"Ruoyan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Hebei Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China"},{"name":"State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China"},{"name":"Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China"},{"name":"Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengfan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Hebei Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China"},{"name":"State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China"},{"name":"Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China"},{"name":"Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.neuron.2018.01.008","article-title":"Neuromodulation of attention","volume":"97","author":"Thiele","year":"2018","journal-title":"Neuron"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, S., Enkhzaya, G., Zhu, B.-H., Chen, J., Wang, Z.-J., Kim, E.-S., and Kim, N.-Y. 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