{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:39:11Z","timestamp":1762252751645,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"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 home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person\u2019s field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the \u03b4 (0~4 Hz) and \u03b8 (4~8 Hz) sub-bands of subjects\u2019 electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated. Then, the motion characteristics are analyzed using the brain network parameters clustering coefficient (C) and global efficiency (G). Meanwhile, the eye movement characteristics in the F3 and F4 channels are identified. Finally, the motion characteristics identified by brain networks and eye movement characteristics can be used to control the home-auxiliary robot platform. The experimental result shows that the accuracy rate of left and right motion recognition using this method is more than 93%. Additionally, the similarity between that autonomous return path and the real path of the home-auxiliary robot reaches up to 0.89.<\/jats:p>","DOI":"10.3390\/s18061779","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T08:52:03Z","timestamp":1528102323000},"page":"1779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Study of the Home-Auxiliary Robot Based on BCI"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7209-7997","authenticated-orcid":false,"given":"Fuwang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China"}]},{"given":"Xiaolei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China"}]},{"given":"Rongrong","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}]},{"given":"Guangbin","family":"Sun","sequence":"additional","affiliation":[{"name":"Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.procs.2018.01.079","article-title":"The Control System Based on Extended BCI for a Robotic Wheelchair","volume":"123","author":"Voznenko","year":"2018","journal-title":"Procedia Comput. 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