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Feature fusion can lead to more discriminative features, but simple fusion of features from different embedding spaces leading to the model global loss is not easily convergent and ignores the complementarity of features. Considering the similarity and category contribution of different sub-band features, we propose a multi-band centroid contrastive reconstruction fusion network (MB-CCRF). We obtain multi-band spatio-temporal features by frequency division, preserving the task-related rhythmic features of different EEG signals; use a multi-stream cross-layer connected convolutional network to perform a deep feature representation for each sub-band separately; propose a centroid contrastive reconstruction fusion module, which maps different sub-band and category features into the same shared embedding space by comparing with category prototypes, reconstructing the feature semantic structure to ensure that the global loss of the fused features converges more easily. Finally, we use a learning mechanism to model the similarity between channel features and use it as the weight of fused sub-band features, thus enhancing the more discriminative features, suppressing the useless features. The experimental accuracy is 79.96% in the BCI competition \u2163-\u2161a dataset. Moreover, the classification effect of sub-band features of different subjects is verified by comparison tests, the category propensity of different sub-band features is verified by confusion matrix tests and the distribution in different classes of each sub-band feature and fused feature are showed by visual analysis, revealing the importance of different sub-band features for the EEG-based MI classification task.&lt;\/p&gt;\n\n\t      &lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2023912","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T08:03:25Z","timestamp":1700121805000},"page":"20624-20647","source":"Crossref","is-referenced-by-count":4,"title":["A multi-band centroid contrastive reconstruction fusion network for motor imagery electroencephalogram signal decoding"],"prefix":"10.3934","volume":"20","author":[{"given":"Jiacan","family":"Xu","sequence":"first","affiliation":[{"name":"The College of Engineering Training and Innovation, Shenyang Jianzhu University, Shenyang 110000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donglin","family":"Li","sequence":"additional","affiliation":[{"name":"The College of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhou","sequence":"additional","affiliation":[{"name":"The College of Engineering Training and Innovation, Shenyang Jianzhu University, Shenyang 110000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunsheng","family":"Li","sequence":"additional","affiliation":[{"name":"The College of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zinan","family":"Wang","sequence":"additional","affiliation":[{"name":"The College of Engineering Training and Innovation, Shenyang Jianzhu University, Shenyang 110000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghao","family":"Tong","sequence":"additional","affiliation":[{"name":"The College of Engineering Training and Innovation, Shenyang Jianzhu University, Shenyang 110000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2023912-1","doi-asserted-by":"publisher","unstructured":"J. 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