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Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10\u201010 system, we select an appropriate type of ANN which reaches 80\u2009\u00b1\u200910% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73\u2009\u00b1\u200915%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time\u2010frequency structure of EEG signals and find that motor\u2010related features associated with left and right leg motor imagery are more pronounced in the mu (8\u201313\u2009Hz) and delta (1\u20135\u2009Hz) brainwaves than in the high\u2010frequency beta brainwave (15\u201330\u2009Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low\u2010pass filter with different cutoffs. We demonstrate that the filtration of high\u2010frequency spectral components significantly enhances the classification performance (up to 90\u2009\u00b1\u20095% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain\u2010computer interfaces for untrained subjects.<\/jats:p>","DOI":"10.1155\/2018\/9385947","type":"journal-article","created":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T23:45:16Z","timestamp":1533167116000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Artificial Neural Network Classification of Motor\u2010Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4632-6896","authenticated-orcid":false,"given":"Vladimir A.","family":"Maksimenko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-5717","authenticated-orcid":false,"given":"Semen A.","family":"Kurkin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1850-2394","authenticated-orcid":false,"given":"Elena N.","family":"Pitsik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vyacheslav Yu.","family":"Musatov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2102-164X","authenticated-orcid":false,"given":"Anastasia E.","family":"Runnova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatyana Yu.","family":"Efremova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2787-2530","authenticated-orcid":false,"given":"Alexander E.","family":"Hramov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2471-2507","authenticated-orcid":false,"given":"Alexander N.","family":"Pisarchik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aa525f"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0172400"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncpneuro0750"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781351231954"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/2326263X.2015.1008956"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0148942"},{"key":"e_1_2_9_7_2","volume-title":"Fisch and Spehlmann\u2032s EEG Primer: Basic Principles of Digital and Analog EEG","author":"Fisch B. 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