{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T11:37:10Z","timestamp":1791286630852,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study introduces an integrated computational environment that leverages Brain\u2013Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human\u2013Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system\u2019s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.<\/jats:p>","DOI":"10.3390\/s24206759","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"6759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Thought-Controlled Computer Applications: A Brain\u2013Computer Interface System for Severe Disability Support"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1455-439X","authenticated-orcid":false,"given":"Kais","family":"Belwafi","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, College of Computing & Informatics, University of Sharjah, Sharjah 26666, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0928-7963","authenticated-orcid":false,"given":"Fakhreddine","family":"Ghaffari","sequence":"additional","affiliation":[{"name":"\u00c9quipes de Traitement de l\u2019Information et Syst\u00e8mes, UMR 8051, CY Cergy Paris Universit\u00e9, \u00c9cole Nationale Sup\u00e9rieure de l\u2019Electronique et de ses Applications (ENSEA), Centre National de la Recherche Scientifique (CNRS), 95000 Cergy, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2022, August 01). 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