{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T08:37:23Z","timestamp":1792917443669,"version":"4.5.0"},"reference-count":38,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Key Laboratory of Flight Techniques and Flight Safety, CAAC","award":["F2024KF12C"],"award-info":[{"award-number":["F2024KF12C"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Science Progress"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p>To improve short-window SSVEP recognition, we integrated adaptive temporal local weighting (ATLW) with filter-bank ensemble task-related component analysis (FB-eTRCA). ATLW estimates local discriminability from training trials, converts window scores into sample weights, and applies the training-derived weights to training and test trials within each cross-validation fold. Experiments were conducted on two public 40-target SSVEP datasets: the Benchmark dataset containing 35 subjects and six blocks, and the BETA database containing 70 subjects and four blocks. Seven observation windows ranging from 0.2 to 1.4 s were evaluated using subject-specific leave-one-block-out validation, with six folds for the Benchmark dataset and four folds for the BETA database. On the Benchmark dataset, only the 0.2-s accuracy gain remained significant after Holm correction (44.79% vs 44.07%; adjusted p = 0.0300); other windows were nonsignificant. On the Benchmark dataset, ablation analysis showed that adaptive weighting was more stable than fixed temporal weighting. After Holm correction, eTRCA+ATLW significantly outperformed eTRCA at 0.4 s (gain: 0.86 percentage points; adjusted p = 0.0023), but not at 0.2 s, supporting transferability to another TRCA-based decoder. EEGNet and BiLSTM were included as representative CNN and RNN baselines; both performed below the TRCA-family methods under the present subject-specific limited-calibration protocol. On the BETA database, FB-eTRCA+ATLW remained close to FB-eTRCA across all seven observation windows without BETA-specific parameter retuning, but no paired difference was statistically significant. These findings indicate that ATLW is a lightweight temporal refinement that can be applied to more than one TRCA-based decoder. Its measurable benefit may depend on SSVEP signal quality and the amount of available subject-specific calibration data. Accuracy increased with longer windows, whereas the information transfer rate peaked at 0.6 s on the Benchmark dataset. Therefore, no observation window was optimal for both accuracy and communication speed.<\/jats:p>","DOI":"10.1177\/00368504261501367","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:07:22Z","timestamp":1792685242000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive local temporal weighting for short-window EEG-based SSVEP recognition using FB-eTRCA"],"prefix":"10.1177","volume":"109","author":[{"given":"Guozhu","family":"Zhao","sequence":"first","affiliation":[{"name":"Civil Aviation Flight University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8359-0732","authenticated-orcid":false,"given":"Zijing","family":"Feng","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehua","family":"Wang","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,22]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2002.803536"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/MEMB.2008.923958"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065714500191"},{"key":"e_1_3_5_5_2","first-page":"E6058","article-title":"High-speed spelling with a noninvasive brain\u2013computer interface","volume":"112","author":"Chen X","year":"2015","unstructured":"Chen X, Wang Y, Nakanishi M, et al. 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