{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:06:49Z","timestamp":1779325609884,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"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>Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16\u201364 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.<\/jats:p>","DOI":"10.3390\/s21082779","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"2779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram"],"prefix":"10.3390","volume":"21","author":[{"given":"Navjodh","family":"Dhillon","sequence":"first","affiliation":[{"name":"Computing and Software Systems, University of Washington, Bothell, WA 98011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agustinus","family":"Sutandi","sequence":"additional","affiliation":[{"name":"Computing and Software Systems, University of Washington, Bothell, WA 98011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manoj","family":"Vishwanath","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miranda","family":"Lim","sequence":"additional","affiliation":[{"name":"VA Portland Health Care System, Portland, OR 97239, USA"},{"name":"Department of Neurology, Oregon Health and Science University, Portland, OR 97239, 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 92697, USA"},{"name":"Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7039-2589","authenticated-orcid":false,"given":"Dong","family":"Si","sequence":"additional","affiliation":[{"name":"Computing and Software Systems, University of Washington, Bothell, WA 98011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","unstructured":"Traumatic Brain Injury Information Page (2021, January 07). National Institute of Neurological Disorders and Stroke, Available online: https:\/\/www.ninds.nih.gov\/Disorders\/All-Disorders\/Traumatic-Brain-Injury-Information-Page."},{"key":"ref_2","unstructured":"(2021, January 05). TBI: Get the Facts|Concussion|Traumatic Brain Injury|CDC Injury Center, Available online: https:\/\/www.cdc.gov\/traumaticbraininjury\/get_the_facts.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"215ra173","DOI":"10.1126\/scitranslmed.3007092","article-title":"Dietary Therapy Mitigates Persistent Wake Deficits Caused by Mild Traumatic Brain Injury","volume":"5","author":"Lim","year":"2013","journal-title":"Sci. Transl. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1602\/neurorx.2.2.372","article-title":"Neuroimaging in Traumatic Brain Imaging","volume":"2","author":"Lee","year":"2005","journal-title":"NeuroRx"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1097\/00001199-200501000-00003","article-title":"Two Decades of Advances in Understanding of Mild Traumatic Brain Injury","volume":"20","author":"Ruff","year":"2005","journal-title":"J. Head Trauma Rehabil."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"O\u2019Neil, M.E., Carlson, K., Storzbach, D., Brenner, L., Freeman, M., Qui\u00f1ones, A., Motu\u2019apuaka, M., Ensley, M., and Kansagara, D. (2013). Complications of Mild Traumatic Brain Injury in Veterans and Military Personnel: A Systematic Review. VA Evidence-Based Synthesis Program Reports, Department of Veterans Affairs.","DOI":"10.1017\/S135561771300146X"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/16501960410023877","article-title":"Methodological issues and research recommendations for mild traumatic brain injury: The who collaborating centre task force on mild traumatic brain injury","volume":"36","author":"Carroll","year":"2004","journal-title":"J. Rehabil. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110","DOI":"10.3171\/jns.1987.67.1.0110","article-title":"A fluid percussion model of experimental brain injury in the rat","volume":"67","author":"Dixon","year":"1987","journal-title":"J. Neurosurg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1002\/cpt.1077","article-title":"Brain Monitoring Devices in Neuroscience Clinical Research: The Potential of Remote Monitoring Using Sensors, Wearables, and Mobile Devices","volume":"104","author":"Byrom","year":"2018","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vishwanath, M., Jafarlou, S., Shin, I., Lim, M.M., Dutt, N., Rahmani, A.M., and Cao, H. (2020). Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice. Sensors, 20.","DOI":"10.3390\/s20072027"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sandsmark, D.K., Elliott, J.E., and Lim, M.M. (2017). Sleep-Wake Disturbances After Traumatic Brain Injury: Synthesis of Human and Animal Studies. Sleep, 40.","DOI":"10.1093\/sleep\/zsx044"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.nbscr.2016.06.001","article-title":"EEG slow waves in traumatic brain injury: Convergent findings in mouse and man","volume":"2","author":"Modarres","year":"2017","journal-title":"Neurobiol. Sleep Circadian Rhythm."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3389\/fnhum.2015.00011","article-title":"Traumatic Brain Injury Detection Using Electrophysiological Methods","volume":"9","author":"Rapp","year":"2015","journal-title":"Front. Hum. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4247","DOI":"10.1038\/s41598-018-38102-4","article-title":"Strong correlation of novel sleep electroencephalography coherence markers with diagnosis and severity of posttraumatic stress disorder","volume":"9","author":"Modarres","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sutandi, A., Dhillon, N., Lim, M., Cao, H., and Si, D. (2020, January 5). Detection of Traumatic Brain Injury Using Single Channel Electroencephalogram in Mice. Proceedings of the 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA.","DOI":"10.1109\/SPMB50085.2020.9353651"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Konstantinidis, E., Conci, N., Bamparopoulos, G., Sidiropoulos, E., De Natale, F., and Bamidis, P. (2015). Introducing Neuroberry, a Platform for Pervasive EEG Signaling in the IoT Domain. Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare (MOBIHEALTH\u201915), ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).","DOI":"10.4108\/eai.14-10-2015.2261698"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Arif, R., Wijaya, S., and Gani, H. (2018, January 1\u20133). Design of EEG Data Acquisition System Based on Raspberry Pi 3 for Acute Ischemic Stroke Identification. Proceedings of the 2018 International Conference on Signals and Systems (ICSigSys), Bali, Indonesia.","DOI":"10.1109\/ICSIGSYS.2018.8372771"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zgallai, W., Brown, J.T., Ibrahim, A., Mahmood, F., Mohammad, K., Khalfan, M., Mohammed, M., Salem, M., and Hamood, N. (April, January 26). Deep Learning AI Application to an EEG Driven BCI Smart Wheelchair. Proceedings of the 2019 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates.","DOI":"10.1109\/ICASET.2019.8714373"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Albert, B., Zhang, J., Noyvirt, A., Setchi, R., Sjaaheim, H., Velikova, S., and Strisland, F. (August, January 31). Automatic EEG Processing for the Early Diagnosis of Traumatic Brain Injury. Proceedings of the 2016 World Automation Congress (WAC), Rio Grande, Puerto Rico.","DOI":"10.1109\/WAC.2016.7582957"},{"key":"ref_21","unstructured":"(2021, January 05). MCP3008\u2014Analog to Digital Converters. Available online: https:\/\/www.microchip.com\/wwwproducts\/en\/MCP3008."},{"key":"ref_22","unstructured":"(2021, January 06). MCP4725\u2014Digital to Analog Converters. Available online: https:\/\/www.microchip.com\/wwwproducts\/en\/MCP4725."},{"key":"ref_23","unstructured":"(2021, March 21). XGBoost Parameters Xgboost 1.4.0-SNAPSHOT Documentation. Available online: https:\/\/xgboost.readthedocs.io\/en\/latest\/parameter.html."},{"key":"ref_24","unstructured":"(2021, March 22). dmlc\/xgboost. Available online: https:\/\/github.com\/dmlc\/xgboost."},{"key":"ref_25","unstructured":"(2021, March 22). scipy.signal.welch. SciPy v1.6.1 Reference Guide. Available online: https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.signal.welch.html#scipy.signal.welch."},{"key":"ref_26","unstructured":"(2021, January 25). Cyton Biosensing Board (8-Channels). Available online: https:\/\/shop.openbci.com\/products\/cyton-biosensing-board-8-channel."},{"key":"ref_27","unstructured":"(2021, January 25). brainHat: Raspberry Pi + OpenBCI = Plug and Play LSL. Available online: https:\/\/openbci.com\/community\/brainhat-raspberry-pi-openbci-plug-and-play-lsl\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2779\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:48:12Z","timestamp":1760161692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2779"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,15]]},"references-count":27,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21082779"],"URL":"https:\/\/doi.org\/10.3390\/s21082779","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,15]]}}}