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However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.<\/jats:p>","DOI":"10.1155\/2021\/9959195","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T19:05:13Z","timestamp":1631905513000},"page":"1-11","source":"Crossref","is-referenced-by-count":3,"title":["Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems"],"prefix":"10.1155","volume":"2021","author":[{"given":"Mingwei","family":"Zhang","sequence":"first","affiliation":[{"name":"Physics and Electronic Engineering, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3120-0561","authenticated-orcid":true,"given":"Yao","family":"Hou","sequence":"additional","affiliation":[{"name":"Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongnian","family":"Tang","sequence":"additional","affiliation":[{"name":"Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youjun","family":"Li","sequence":"additional","affiliation":[{"name":"Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"first-page":"4626","article-title":"EEG-based mental task classification: linear and nonlinear classification of movement imagery","author":"A. 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