{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T19:38:21Z","timestamp":1768419501305,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,14]],"date-time":"2019-12-14T00:00:00Z","timestamp":1576281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2017R1C1B5017918"],"award-info":[{"award-number":["2017R1C1B5017918"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1D1A1A02050292"],"award-info":[{"award-number":["NRF-2018R1D1A1A02050292"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell\u2019s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.<\/jats:p>","DOI":"10.3390\/s19245533","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:19:38Z","timestamp":1576473578000},"page":"5533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach"],"prefix":"10.3390","volume":"19","author":[{"given":"Heekyung","family":"Yang","sequence":"first","affiliation":[{"name":"Industry-Academy Coorporation Foundation, Sangmyung University, Seoul 03016, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongdae","family":"Han","sequence":"additional","affiliation":[{"name":"Departement of Computer Science, Sangmyung University, Seoul 03016, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyungha","family":"Min","sequence":"additional","affiliation":[{"name":"Departement of Computer Science, Sangmyung University, Seoul 03016, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A Database for Emotion Analysis; Using Physiological Signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. 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