{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T16:49:39Z","timestamp":1779122979500,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute Of Neurological Disorders","doi-asserted-by":"publisher","award":["R03NS127044"],"award-info":[{"award-number":["R03NS127044"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1\u201370 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p &lt; 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient\u2019s MRI without requiring long-term iEEG inspection.<\/jats:p>","DOI":"10.3390\/a16120567","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T08:12:57Z","timestamp":1702627977000},"page":"567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?"],"prefix":"10.3390","volume":"16","author":[{"given":"Navaneethakrishna","family":"Makaram","sequence":"first","affiliation":[{"name":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1734-9847","authenticated-orcid":false,"given":"Sarvagya","family":"Gupta","sequence":"additional","affiliation":[{"name":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Pesce","sequence":"additional","affiliation":[{"name":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey","family":"Bolton","sequence":"additional","affiliation":[{"name":"Department of Neurology, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4546-6790","authenticated-orcid":false,"given":"Scellig","family":"Stone","sequence":"additional","affiliation":[{"name":"Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9144-3461","authenticated-orcid":false,"given":"Daniel","family":"Haehn","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Massachusetts Boston, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Pomplun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Massachusetts Boston, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos","family":"Papadelis","sequence":"additional","affiliation":[{"name":"Jane and John Justin Institute for Mind Health, Cook Children\u2019s Health Care System, Fort Worth, TX 76104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6373-1068","authenticated-orcid":false,"given":"Phillip","family":"Pearl","sequence":"additional","affiliation":[{"name":"Department of Neurology, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Rotenberg","sequence":"additional","affiliation":[{"name":"Department of Neurology, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1005-4013","authenticated-orcid":false,"given":"Patricia Ellen","family":"Grant","sequence":"additional","affiliation":[{"name":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1235-3052","authenticated-orcid":false,"given":"Eleonora","family":"Tamilia","sequence":"additional","affiliation":[{"name":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"},{"name":"Department of Neurology, Boston Children\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1109\/TNSRE.2017.2755770","article-title":"Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG","volume":"25","author":"Antoniades","year":"2017","journal-title":"IEEE Trans. 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