{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:20:56Z","timestamp":1778347256362,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T00:00:00Z","timestamp":1633564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation (IITP)","award":["2017-0-00432"],"award-info":[{"award-number":["2017-0-00432"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1A2C1003399"],"award-info":[{"award-number":["2019R1A2C1003399"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Motor imagery (MI) brain\u2013computer interfaces (BCIs) have been used for a wide variety of applications due to their intuitive matching between the user\u2019s intentions and the performance of tasks. Applying dry electroencephalography (EEG) electrodes to MI BCI applications can resolve many constraints and achieve practicality. In this study, we propose a multi-domain convolutional neural networks (MD-CNN) model that learns subject-specific and electrode-dependent EEG features using a multi-domain structure to improve the classification accuracy of dry electrode MI BCIs. The proposed MD-CNN model is composed of learning layers for three domain representations (time, spatial, and phase). We first evaluated the proposed MD-CNN model using a public dataset to confirm 78.96% classification accuracy for multi-class classification (chance level accuracy: 30%). After that, 10 healthy subjects participated and performed three classes of MI tasks related to lower-limb movement (gait, sitting down, and resting) over two sessions (dry and wet electrodes). Consequently, the proposed MD-CNN model achieved the highest classification accuracy (dry: 58.44%; wet: 58.66%; chance level accuracy: 43.33%) with a three-class classifier and the lowest difference in accuracy between the two electrode types (0.22%, d = 0.0292) compared with the conventional classifiers (FBCSP, EEGNet, ShallowConvNet, and DeepConvNet) that used only a single domain. We expect that the proposed MD-CNN model could be applied for developing robust MI BCI systems with dry electrodes.<\/jats:p>","DOI":"10.3390\/s21196672","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"6672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-Domain Convolutional Neural Networks for Lower-Limb Motor Imagery Using Dry vs. Wet Electrodes"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4200-0650","authenticated-orcid":false,"given":"Ji-Hyeok","family":"Jeong","sequence":"first","affiliation":[{"name":"Biomedical Research Division, Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Korea"},{"name":"Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-Hyuk","family":"Choi","sequence":"additional","affiliation":[{"name":"Biomedical Research Division, Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Korea"},{"name":"Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2731-3915","authenticated-orcid":false,"given":"Keun-Tae","family":"Kim","sequence":"additional","affiliation":[{"name":"Biomedical Research Division, Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song-Joo","family":"Lee","sequence":"additional","affiliation":[{"name":"Biomedical Research Division, Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Korea"},{"name":"Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong-Joo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea"},{"name":"Department of Neurology, Korea University College of Medicine, Seoul 02841, Korea"},{"name":"Department of Artificial Intelligence, Korea University, Seoul 02841, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9527-0609","authenticated-orcid":false,"given":"Hyung-Min","family":"Kim","sequence":"additional","affiliation":[{"name":"Biomedical Research Division, Bionics Research Center, Korea Institute of Science and Technology, Seoul 02792, Korea"},{"name":"Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1080\/00051144.2019.1570644","article-title":"Brain-Computer Interface in Europe: The thirtieth anniversary","volume":"60","author":"Bozinovski","year":"2019","journal-title":"Automatika"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1146\/annurev.bb.02.060173.001105","article-title":"Toward Direct Brain-Computer Communication","volume":"2","author":"Vidal","year":"1973","journal-title":"Annu. 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