{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T23:41:02Z","timestamp":1775950862329,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T00:00:00Z","timestamp":1556582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Fund for Regional Development (EFRD - or EFRE in German)","award":["GE-1-1-047"],"award-info":[{"award-number":["GE-1-1-047"]}]},{"name":"European Fund for Regional Development (EFRD - or EFRE in German)","award":["IT-1-2-001"],"award-info":[{"award-number":["IT-1-2-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Brain\u2013computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) typically utilize a synchronous approach to identify targets (i.e., after preset time periods the system produces command outputs). Hence, users have only a limited amount of time to fixate a desired target. This hinders the usage of more complex interfaces, as these require the BCI to distinguish between intentional and unintentional fixations. In this article, we investigate a dynamic sliding window mechanism as well as the implementation of software-based stimulus synchronization to enable the threshold-based target identification for the c-VEP paradigm. To further improve the usability of the system, an ensemble-based classification strategy was investigated. In addition, a software-based approach for stimulus on-set determination is proposed, which allows for an easier setup of the system, as it reduces additional hardware dependencies. The methods were tested with an eight-target spelling application utilizing an n-gram word prediction model. The performance of eighteen participants without disabilities was tested; all participants completed word- and sentence spelling tasks using the c-VEP BCI with a mean information transfer rate (ITR) of 75.7 and 57.8 bpm, respectively.<\/jats:p>","DOI":"10.3390\/computers8020033","type":"journal-article","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T08:51:44Z","timestamp":1556614304000},"page":"33","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-2993","authenticated-orcid":false,"given":"Felix","family":"Gembler","sequence":"first","affiliation":[{"name":"Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6555-7617","authenticated-orcid":false,"given":"Ivan","family":"Volosyak","sequence":"additional","affiliation":[{"name":"Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain\u2013Computer Interfaces for Communication and Control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin. 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