{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:09:45Z","timestamp":1776326985915,"version":"3.50.1"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2025,5,22]]},"abstract":"<jats:p>We present EarEOG, a real-time wheelchair control system using around-ear electrooculogram (EOG) signals. Electrodes are placed in standard over-the-ear headphones to improve user comfort. By detecting around-the-ear signals from eye gestures and jaw clenching, EarEOG offers a non-invasive and intuitive approach to low-latency wheelchair control. We describe the methods for signal acquisition, as well as the algorithms used for signal processing and classification. The feasibility, robustness, and low latency of EarEOG were confirmed through two experiments. The algorithm demonstrated a classification accuracy of 94.1% for all motion signals, which further improved to 97.3% when personalized models were applied. To ensure stability, we examined electrode impedance and algorithm accuracy across multiple trials where participants operated simulated wheelchairs while wearing EarEOG. The results indicated that when the electrode impedance was below 1 M\u03a9, all participants successfully controlled the simulated wheelchair. Furthermore, EarEOG demonstrated low latency, with recognition delays of less than 125 ms.<\/jats:p>","DOI":"10.1145\/3725833","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T18:18:53Z","timestamp":1747937933000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["EarEOG: Using Headphones and Around-the-Ear EOG Signals for Real-Time Wheelchair Control"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8046-1694","authenticated-orcid":false,"given":"Peichen","family":"Liu","sequence":"first","affiliation":[{"name":"Technical University of Denmark, Kongens Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7564-2612","authenticated-orcid":false,"given":"Sadasivan","family":"Puthusserypady","sequence":"additional","affiliation":[{"name":"Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1731-9651","authenticated-orcid":false,"given":"I. Scott","family":"MacKenzie","sequence":"additional","affiliation":[{"name":"York University, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3852-9164","authenticated-orcid":false,"given":"Cihan","family":"Uyanik","sequence":"additional","affiliation":[{"name":"Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5594-3645","authenticated-orcid":false,"given":"John Paulin","family":"Hansen","sequence":"additional","affiliation":[{"name":"Technical University of Denmark, Kgs. Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/NER.2015.7146685"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCONS.2018.8663158"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2019.04.002"},{"key":"e_1_2_1_4_1","volume-title":"Unlocking Eye Gestures with Earable Inertial Sensing for Accessible HCI. In 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). IEEE, 828--832","author":"Chugh Garvit","year":"2025","unstructured":"Garvit Chugh, Suchetana Chakraborty, and Sandip Chakraborty. 2025. Unlocking Eye Gestures with Earable Inertial Sensing for Accessible HCI. In 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). 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Wheelchair Control Based on EOG Signals of Eye Blinks and Eye Glances Based on The Decision Tree Method. In 2024 12th International Conference on Information and Communication Technology (ICoICT). 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