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MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine\u2013cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine\u2013cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain\u2013computer interface. <\/jats:p>","DOI":"10.1142\/s0129065717500393","type":"journal-article","created":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T02:31:05Z","timestamp":1502677865000},"page":"1750039","source":"Crossref","is-referenced-by-count":79,"title":["A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain\u2013Computer Interface"],"prefix":"10.1142","volume":"28","author":[{"given":"Yong","family":"Jiao","sequence":"first","affiliation":[{"name":"Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. 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