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In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery\/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.<\/jats:p>","DOI":"10.1515\/bmt-2018-0201","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T09:02:28Z","timestamp":1557997348000},"page":"643-653","source":"Crossref","is-referenced-by-count":7,"title":["Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery\/movement EEG data"],"prefix":"10.1515","volume":"64","author":[{"given":"Funda Kutlu","family":"Onay","sequence":"first","affiliation":[{"name":"Computer Engineering Department , Amasya University , Amasya 05100 , Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cemal","family":"K\u00f6se","sequence":"additional","affiliation":[{"name":"Computer Engineering Department , Karadeniz Technical University , Trabzon , Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"2023033112565212472_j_bmt-2018-0201_ref_001_w2aab3b7c10b1b6b1ab1b5b1Aa","unstructured":"Anupama HS, Cauvery NK, Lingaraju GM. Brain computer interface and its types \u2013 a study. Int J Adv Eng Technol 2012;3:739\u201345."},{"key":"2023033112565212472_j_bmt-2018-0201_ref_002_w2aab3b7c10b1b6b1ab1b5b2Aa","unstructured":"He L, Yu Z, Gu Z, Li Y. Bhattacharyya bound based channel selection for classification of motor imageries in EEG signals. In: Proceedings of Chinese Control and Decision Conference (CCDC\u201909), June 17\u201319, 2009. Guilin, China: IEEE 2009:2353\u20136."},{"key":"2023033112565212472_j_bmt-2018-0201_ref_003_w2aab3b7c10b1b6b1ab1b5b3Aa","doi-asserted-by":"crossref","unstructured":"He L, Hu Y, Li Y, Li D. Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG. 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