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Fisher discriminant analysis-type F-score based on band pass (BP) feature and power spectra density (PSD) feature are employed respectively to select the subject-optimal frequency bands. In the experiments, typical frequency bands related to motor imagery EEG signals, subject-optimal frequency bands and extension frequency bands are employed respectively as the frequency range of the input image of CNN. The better classification performance of extension frequency bands show that CNN can extract optimal feature from frequency information automatically. The classification result also demonstrates that the proposed approach is more competitive in prediction of left\/right hand motor imagery task compared with other state-of-art approaches.<\/jats:p>","DOI":"10.4018\/ijcini.2019070103","type":"journal-article","created":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T15:52:45Z","timestamp":1559836365000},"page":"36-49","source":"Crossref","is-referenced-by-count":8,"title":["Simple Convolutional Neural Network for Left-Right Hands Motor Imagery EEG Signals Classification"],"prefix":"10.4018","volume":"13","author":[{"given":"Geliang","family":"Tian","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}]},{"given":"Yue","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}]}],"member":"2432","reference":[{"key":"IJCINI.2019070103-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2015.01.033"},{"key":"IJCINI.2019070103-1","doi-asserted-by":"publisher","DOI":"10.1109\/IWW-BCI.2017.7858143"},{"key":"IJCINI.2019070103-2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-09330-7_25"},{"key":"IJCINI.2019070103-3","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2012.00039"},{"key":"IJCINI.2019070103-4","unstructured":"Blankertz, B., Dornhege, G., Krauledat, M., Schr\u00f6der, M., Williamson, J., Murray-Smith, R., & M\u00fcller, K. 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