{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:47:04Z","timestamp":1771958824171,"version":"3.50.1"},"reference-count":106,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"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>A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.<\/jats:p>","DOI":"10.3390\/s20133620","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:17:17Z","timestamp":1593429437000},"page":"3620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Brain-Computer Interface-Based Humanoid Control: A Review"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6730-3060","authenticated-orcid":false,"given":"Vinay","family":"Chamola","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics, Birla Institute of Technology &amp; Science, Pilani 333031, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7180-8574","authenticated-orcid":false,"given":"Ankur","family":"Vineet","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics, Birla Institute of Technology &amp; Science, Pilani 333031, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anand","family":"Nayyar","sequence":"additional","affiliation":[{"name":"Graduate School, Duy Tan University, Da Nang 550000, Vietnam"},{"name":"Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2332-8095","authenticated-orcid":false,"given":"Eklas","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Renewable energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,27]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A Survey: Fundamental of EEG","volume":"1","author":"Mantri","year":"2013","journal-title":"Int. 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