{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:24:34Z","timestamp":1777058674493,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,15]],"date-time":"2020-11-15T00:00:00Z","timestamp":1605398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2020-2017-0-01630"],"award-info":[{"award-number":["IITP-2020-2017-0-01630"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG\u2019s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG\u2019s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.<\/jats:p>","DOI":"10.3390\/s20226526","type":"journal-article","created":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T21:48:52Z","timestamp":1605563332000},"page":"6526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0548-170X","authenticated-orcid":false,"given":"Min","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sungnam-si 13306, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3592-7431","authenticated-orcid":false,"given":"Hyunjin","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sungnam-si 13306, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7471-9745","authenticated-orcid":false,"given":"Jin-Hyeok","family":"Park","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Sungnam-si 13306, Korea"}]},{"given":"Seokhwan","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sungnam-si 13306, Korea"}]},{"given":"Youngho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sungnam-si 13306, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,15]]},"reference":[{"key":"ref_1","unstructured":"(2020, September 07). 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