{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:31:55Z","timestamp":1780054315797,"version":"3.54.0"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Lt","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:p> Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping. <\/jats:p>","DOI":"10.1142\/s0129065718500090","type":"journal-article","created":{"date-parts":[[2018,3,19]],"date-time":"2018-03-19T04:22:40Z","timestamp":1521433360000},"page":"1850009","source":"Crossref","is-referenced-by-count":91,"title":["Deep Neural Architectures for Mapping Scalp to Intracranial EEG"],"prefix":"10.1142","volume":"28","author":[{"given":"Andreas","family":"Antoniades","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loukianos","family":"Spyrou","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Edinburgh, EH9 3FB, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Martin-Lopez","sequence":"additional","affiliation":[{"name":"Kingston Hospital NHS FT, London, SE5 9RS, UK"},{"name":"King\u2019s College London, WC2R 2LS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Valentin","sequence":"additional","affiliation":[{"name":"King\u2019s College London, WC2R 2LS, UK"},{"name":"King\u2019s College Hospital, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gonzalo","family":"Alarcon","sequence":"additional","affiliation":[{"name":"King\u2019s College London, WC2R 2LS, UK"},{"name":"Hamad Medical Corporation, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeid","family":"Sanei","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clive Cheong","family":"Took","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2018,8,26]]},"reference":[{"key":"S0129065718500090BIB001","doi-asserted-by":"publisher","DOI":"10.1002\/9781118622162"},{"key":"S0129065718500090BIB002","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065711002870"},{"key":"S0129065718500090BIB003","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065714500154"},{"key":"S0129065718500090BIB004","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065711002912"},{"key":"S0129065718500090BIB005","first-page":"21","volume":"52","author":"Noachtar S.","year":"1999","journal-title":"The International Federation of Clinical Neurophysiology, Electroencephalogr. 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