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Fuzzy Syst."},{"key":"10.1016\/j.bspc.2026.110176_b0205","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/7799793","article-title":"A novel fatigue driving state recognition and warning method based on EEG and EOG signals","volume":"2021","author":"Liu","year":"2021","journal-title":"J. Healthcare Eng."},{"key":"10.1016\/j.bspc.2026.110176_b0210","doi-asserted-by":"crossref","unstructured":"R. Gavas, M.B. S, D. Chatterjee, R.K. Ramakrishnan, V.S. Viraraghavan, A.A. Kumar, M.G. Chandra, Blink rate variability: a marker of sustained attention during a visual task, Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, (2020), pp. 450-455.","DOI":"10.1145\/3410530.3414431"},{"key":"10.1016\/j.bspc.2026.110176_b0215","unstructured":"J. Van Amersfoort, L. Smith, Y.W. Teh, Y. 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