{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T10:41:10Z","timestamp":1778668870934,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,4]],"date-time":"2020-04-04T00:00:00Z","timestamp":1585958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1917105"],"award-info":[{"award-number":["1917105"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today\u2019s world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios.<\/jats:p>","DOI":"10.3390\/s20072027","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T03:58:39Z","timestamp":1586231919000},"page":"2027","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice"],"prefix":"10.3390","volume":"20","author":[{"given":"Manoj","family":"Vishwanath","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92607, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salar","family":"Jafarlou","sequence":"additional","affiliation":[{"name":"Erik Jonsson School of Engineering and Computer Science, Dallas, TX 75201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ikhwan","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92607, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miranda M.","family":"Lim","sequence":"additional","affiliation":[{"name":"VA Portland Health Care System, Portland, OR 97239, USA"},{"name":"Departments of Neurology, Behavioral Neuroscience, Medicine, and Oregon Institute of Occupational Health Sciences, Oregon Health &amp; Science University, Portland, OR 97239, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikil","family":"Dutt","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92607, USA"},{"name":"Department of Computer Science, University of California, Irvine, CA 92607, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir M.","family":"Rahmani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, Irvine, CA 92607, USA"},{"name":"School of Nursing, University of California, Irvine, CA 92607, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4197-7208","authenticated-orcid":false,"given":"Hung","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92607, USA"},{"name":"Department of Biomedical Engineering, University of California, Irvine, CA 92607, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,4]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Estimating the global incidence of traumatic brain injury","volume":"1","author":"Dewan","year":"2018","journal-title":"J. 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