{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:07:38Z","timestamp":1775470058063,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Driver fatigue is a key factor in road accidents worldwide, requiring effective real-time detection mechanisms. Traditional deep neural network (DNN)-based solutions have shown promising results in detecting drowsiness; however, they are often less suitable for real-time applications due to their high computational complexity, risk of overfitting, and reliance on large datasets. Hence, this paper introduces an innovative approach that integrates fast neighbourhood component analysis (FNCA) with a deep neural network (DNN) to enhance the detection of driver drowsiness using electroencephalogram (EEG) data. FNCA is employed to optimize feature representation, effectively highlighting critical features for drowsiness detection, which are then analysed using a DNN to achieve high accuracy in recognizing signs of driver fatigue. Our model has been evaluated on the SEED-VIG dataset and achieves state-of-the-art accuracy: 94.29% when trained on 12 subjects and 90.386% with 21 subjects, surpassing existing methods such as TSception, ConvNeXt LMDA-Net, and CNN + LSTM.<\/jats:p>","DOI":"10.3390\/bdcc9050126","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T09:58:09Z","timestamp":1746698289000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Introducing a Novel Fast Neighbourhood Component Analysis\u2013Deep Neural Network Model for Enhanced Driver Drowsiness Detection"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1731-5944","authenticated-orcid":false,"given":"Sama Hussein","family":"Al-Gburi","sequence":"first","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0409-8803","authenticated-orcid":false,"given":"Kanar Alaa","family":"Al-Sammak","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}]},{"given":"Ion","family":"Marghescu","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}]},{"given":"Claudia Cristina","family":"Oprea","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6096-5793","authenticated-orcid":false,"given":"Ana-Maria Claudia","family":"Dr\u0103gulinescu","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8455-6177","authenticated-orcid":false,"given":"George","family":"Suciu","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"},{"name":"BEIA Consult International, 060042 Bucharest, Romania"}]},{"given":"Khattab M. Ali","family":"Alheeti","sequence":"additional","affiliation":[{"name":"Department of Computer Networking System, College of Computer Sciences, and Information Technology, University of Anbar, Ramadi 31001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7198-695X","authenticated-orcid":false,"given":"Nayef A. M.","family":"Alduais","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja 86400, Malaysia"}]},{"given":"Nawar Alaa Hussein","family":"Al-Sammak","sequence":"additional","affiliation":[{"name":"College of Education for Pure Science, University of Kerbala, Babylon 56001, Iraq"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Albadawi, Y., Takruri, M., and Awad, M. (2022). A Review of Recent Developments in Driver Drowsiness Detection Systems. 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